# A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions

Ons Aouedi, Thai-Hoc Vu, Alessio Sacco, Dinh Nguyen, Kandaraj Piamrat, Guido Marchetto, Quoc-Viet Pham

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Ons Aouedi, Thai-Hoc Vu, Alessio Sacco, Dinh Nguyen, Kandaraj Piamrat, et al.. A Survey on Intel- ligent Internet of Things: Applications, Security, Privacy, and Future Directions. Communications Surveys and Tutorials, IEEE Communications Society, 2024, pp.1-1. 10.1109/COMST.2024.3430368 . hal-04811049

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# A Survey on Intelligent Internet of Things:
Applications, Security, Privacy,
and Future Directions

Ons Aouedi<sup>D</sup>, Thai-Hoc Vu<sup>D</sup>, Graduate Student Member, IEEE, Alessio Sacco<sup>D</sup>, Member, IEEE,

Dinh C. Nguyen<sup>D</sup>, Member, IEEE, Kandaraj Piamrat<sup>D</sup>, Guido Marchetto<sup>D</sup>, Senior Member, IEEE,
and Quoc-Viet Pham<sup>D</sup>, Senior Member, IEEE

**Abstract**—The rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area.

**Index Terms**—Internet of Things, artificial intelligence, wireless networks, industrial applications, security, privacy.

Manuscript received 22 August 2023; revised 18 April 2024; accepted 3 June 2024. Date of publication 18 July 2024; date of current version 2 May 2025. This work was supported in part by the European Union Horizon-CL4-2021 Research and Innovation Program under Grant 101070181 (TALON). The work of Quoc-Viet Pham was supported in part by the NDS Discipline Development Fund at the School of Computer Science and Statistics, Trinity College Dublin. (Corresponding author: Quoc-Viet Pham.)

Ons Aouedi is with the SnT, SIGCOM, University of Luxembourg, 1855 Luxembourg City, Luxembourg (e-mail: ons.aouedi@uni.lu).

Thai-Hoc Vu is with the Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea (e-mail: vuthaihoc1995@gmail.com).

Alessio Sacco and Guido Marchetto are with the DAUIN, Politecnico di Torino, 10129 Turin, Italy (e-mail: alessio_sacco@polito.it; guido.marchetto@polito.it).

Dinh C. Nguyen is with the Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899 USA (e-mail: Dinh.Nguyen@uah.edu).

Kandaraj Piamrat is with the Nantes University, École Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France (e-mail: kandaraj.piamrat@ls2n.fr).

Quoc-Viet Pham is with the School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Dublin 2, D02 PN40 Ireland (e-mail: viet.pham@tcd.ie).

Digital Object Identifier 10.1109/COMST.2024.3430368

## I. INTRODUCTION

THE INTERNET of Things (IoT) has evolved in recent years as a paradigm driving the development of modern companies and smart cities [1]. IoT enables connections between distributed devices, such as mobile phones, tablets, and computers, which sense and transfer data from external environments to serve end users. The concept of IoT relies mostly on communication between devices for local services, for example, collaborative data collection, and interconnections between devices and the server, for example, cloud servers, edge servers, or data centers, for high-level services, such as data management and network monitoring.

Modern IoT applications require new service standards and high quality of service, where machine learning (ML) and artificial intelligence (AI) techniques have been integrated across the complete data lifecycle, beginning at the point of data generation or collection by IoT devices, all the way to its utilization by end-users. The integration of intelligent solutions with ML/AI in IoT networks forms a new networking paradigm called Intelligent IoT (IIoT),<fnref n="1" /> which has transformed IoT applications such as intelligent healthcare, intelligent transportation, and smart industries [2]. Specifically, IIoT opens up a multitude of opportunities for device advancement, such as equipping local IoT devices with on-device intelligence powered by integrated AI models, and service provision, which includes intelligent data transmission and AI-assisted data management [3]. Leveraging AI in IoT networks paves the way for economical network operations and increases user experience. This empowers consumers in the IoT to harness the benefits of smart functions such as traffic prediction in intelligent vehicular IoT networks and automated disease detection in healthcare-focused IoT systems [4]. Moreover, AI integration into IoT applications presents a broad spectrum of commercial benefits. These include enhanced operational efficiency through intelligent automation and pattern recognition, improved precision cost by eliminating human errors, predictive maintenance to save

<footnote n="1">¹The acronym “IIoT” can refer to both “Industrial Internet of Things” and “Intelligent Internet of Things”. In the context of this paper, “Industrial IoT” refers to the application of IoT technologies in industrial settings for purposes such as improving productivity and efficiency, while “IIoT” refers to Intelligent IoT that signifies the incorporation of AI and ML methodologies to make devices and IoT systems smarter and more autonomous</footnote>
costs and avoid business disruptions, enhanced customer service for increased satisfaction, and increased scalability to handle extensive IoT ecosystems [5]. Commercial applications of AI in IoT are now prevalent across various sectors. For example, manufacturing robots equipped with AI and sensors improve processes and efficiency. Autonomous vehicles use AI to predict behaviors and optimize driving conditions. In retail, AI-driven analytics and smart devices like intelligent shopping carts and security cameras improve customer service and operational efficiency. In healthcare, wearable devices use AI to monitor and provide insights into health metrics, promoting proactive healthcare. Finally, smart agriculture sensors apply AI to optimize resource use and crop yields, demonstrating the transformative potential of AI in IoT across diverse industries.

## A. State-of-the-Art and Our Contributions

Recent concerns about the security and privacy of the IoT have inspired numerous reviews and analyses. For example, in their work referenced as [6], the authors presented a comprehensive overview of the IoT, including aspects such as system architecture, enabling technologies, and challenges related to security and privacy. This study also delved into the integration of fog and edge computing and its real-world applications. In addition to this, there has been a marked interest in the potential of ML and Deep Learning (DL)-based models within the IoT domain. These models are particularly notable for their unique ability to tackle complex problems, and various surveys have emerged that explore ML/DL-based models for IoT systems from different perspectives. For instance, the authors in [7] and [8] examined the challenges involved in utilizing ML/DL models for enhancing IoT security. Furthermore, the work in [9] provided information on the use of these models to address privacy issues within IoT, focusing on concerns such as scalability, interoperability, and resource constraints such as computational power and energy efficiency. On the other hand, [10] offered an extensive review of ML-based Intrusion Detection Systems (IDS), including the principles, benefits, and drawbacks of DL-based and RL-based IDS, with particular attention to platform-specific IDS applications. The role of deep RL (DRL) within IoT was highlighted in [11] and [12], with an emphasis on the application of DRL algorithms to manage communication, computing, caching, and control in various IoT scenarios.

The complexities related to IoT security protection and the potential of ML in addressing these challenges were further explored in [13]. The study also noted the scalability of IoT-enabled botnets, pinpointing this as a significant cybersecurity challenge. In a pioneering survey, [14] detailed DL techniques and forensic methods specific to botnet detection within IoT environments. Furthermore, the use of DL in industrial IoT was discussed in [15], encompassing areas such as smart manufacturing, smart meters, and smart agriculture. Lastly, [16] looked at the deployment of ML and DL models in various layers and nodes of IoT healthcare architectures. A comprehensive comparison of these works and our research

can be found in Table I, providing a summary of existing surveys and contributions in the field.

Despite the extensive study of IIoT in the existing literature, there appears to be a notable absence of comprehensive reviews focused on the application of ML, DL, Federated Learning (FL), and DRL to address security and privacy concerns within IoT systems. To fill this research gap, we provide a review of IIoT systems. Specifically, we provide a state-of-the-art survey on IIoT applications in smart healthcare, smart cities, smart transportation, and smart industries. In addition, we focus on highlighting security vulnerabilities and privacy threats in the IIoT. The contributions of our work can be summarized as follows.

* We present a review of the state-of-the-art IIoT applications, where the use of AI techniques in IoT is investigated and analyzed within different domains, including smart healthcare, smart cities, smart transportation, and smart industry.

* We identify and discuss key security issues in IIoT networks and systems, with a focus on network attacks, confidentiality, integrity, and intrusion. Potential countermeasures are also provided to address these security issues.

* We study privacy concerns in current IIoT networks and applications in three main domains: data privacy leakage, location privacy leakage, and model privacy leakage.

* We highlight several key technical challenges and discuss promising directions in IIoT to provide more insight into future research in this important area.

## B. Structure of the Survey

We summarize the structure of this survey in Fig. 1. We start by examining current studies on IIoT in Section II and the applications and usages of IIoT in Section III. Then, we focus on two of the main issues when dealing with such a large amount of data: security (Section IV) and privacy (Section V). Finally, we summarize the challenges of current solutions in Section VI, and conclude the paper in Section VII. A list of key acronyms and abbreviations used throughout the paper is provided in Table II.

## II. INTELLIGENT INTERNET OF THINGS: STATE-OF-THE-ART

In the following section, we provide an in-depth look at the cutting-edge developments in ML, DL, FL, and DRL within the context of IoT systems. Additionally, we explore and discuss the concepts and prospects of integrating these technologies. Fig. 2 shows the classification and the relationship of the main AI techniques.

### A. Machine/Deep Learning-Based IIoT

AI is a pioneering science that has been in development since 1956. Drawing on knowledge from a diverse array of disciplines such as philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, cybernetics, and linguistics, AI has made significant strides over the years [18]. ML, a branch of AI, focuses on the
<table><caption>TABLE I
SUMMARY OF RELATED REVIEWS ON IIOT</caption><thead><tr><td>References</td><td>Contributions</td><td>Limitations</td></tr></thead><tbody><tr><td>[6]</td><td>A survey of IoT with respect to system architecture, enabling technologies, security, and privacy issues, along with the integration of fog/edge computing and its applications.</td><td>The application of ML in IoT networks has not been explored.</td></tr><tr><td>[7], [8]</td><td>A survey on ML/DL in mitigating the security threats in IoT including the discussion of using ML/DL to secure IoT systems.</td><td>The use of FL in IoT networks and privacy issues have not been discussed.</td></tr><tr><td>[9]</td><td>A survey on the use of ML to address privacy issues of IoT, including scalability, interoperability and resource limitations, such as computation and energy.</td><td>Security and FL-based solutions have not been presented.</td></tr><tr><td>[10]</td><td>A survey on ML-based IDS for IoT systems.</td><td>The privacy issues have not been explored and discussed.</td></tr><tr><td>[11], [12]</td><td>A review of the recent studies on using DRL algorithms to address communication, computing, caching, and control problems so as to enable a wide variety of IoT applications.</td><td>The paper only focuses on deep reinforcement learning in IoT systems.</td></tr><tr><td>[13]</td><td>A systematic review of the use of AI in solving the IoT security problems.</td><td>The paper only focuses on the use of AI with security aspects in IoT, while its roles with privacy issues have not been presented.</td></tr><tr><td>[14]</td><td>A review of DL mechanisms used to investigate botnets and their applicability in the IoT systems.</td><td>The paper only focuses on DL mechanisms for botnets in IoT.</td></tr><tr><td>[15]</td><td>A survey on DL techniques and their use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture.</td><td>The IIoT security aspects were partially covered and the privacy issues have not been discussed.</td></tr><tr><td>[16]</td><td>A review of edge intelligence in healthcare IoT, including architecture and frameworks.</td><td>The security, threats, and privacy issues have not been explored and discussed.</td></tr><tr><td>[17]</td><td>A survey on edge learning vulnerabilities and defenses for 6G-enabled IoT.</td><td>The IIoT applications and privacy issues have not been explored.</td></tr><tr><td>This paper</td><td>An extensive survey on IIoT. Particularly, - We extensively discuss the role of ML, DL, FL, and DRL in IoT applications including smart healthcare, smart city, smart transportation, and smart industry. - We identify and discuss the security and privacy issues in IIoT systems. - We provide a taxonomy table and key lessons learned. The research challenges and directions are also highlighted.</td><td>-</td></tr></tbody></table>

development of computer systems that can learn and improve from experience, as outlined in [19]. Rather than following explicitly programmed instructions, ML strives to generalize the examples provided in a training set. This approach can be described as “programming by example” [20]. ML has emerged as an area of significant interest for both academia and industry, with major players such as Google, Apple, Facebook, Netflix, and Amazon investing in its development. In contrast to traditional programming, the objective of ML in general is to find “rules” or build a model using the training data. The model is then applied to unseen data for prediction/classification and automated decision-making processes. Mathematically, the general formula for a predictive model in ML can be written as follows:

$$y = f(x; \theta),$$

where y is the prediction, x is the input data, f(·) is the model, and θ represents the model parameters.

ML can be classified as either *shallow learning* or *deep learning*. The performance of shallow models is heavily dependent on the representation of features, while DL encompasses deep structured learning, hierarchical learning, and

deep feature learning [21]. In recent years, DL has gained significant popularity in IoT systems, mainly due to its exceptional performance when handling large datasets. The improvement in DL-based models can be attributed to their inherent non-linearity. This allows for an increase in model size and consequently the ability to scale efficiently with larger datasets. Furthermore, the vast amounts of data generated by IoT networks have been a driving force behind the success of DL. DL-based models employ intricate combinations of linear and non-linear functions to effectively learn pertinent features [22]. These models consist of two main components, which are activation functions and layers. Activation functions (e.g., ReLU, sigmoid) are responsible for determining the output of neurons given input data. Herein, a neural network function can be represented as

$$y = \sigma(Wx + b),$$

where σ is a non-linear activation function, W is the weight matrix, x is the input vector and b is the bias vector.

Layers in DL refer to organized sets of neurons, which can be fully connected, convolutional, pooling, or recurrent, depending on the specific architecture. For instance,
<figure><img src="image_2.png" /><figcaption>Fig. 1. The outline of this survey paper.</figcaption></figure>

multilayer perceptron (MLP) networks and autoencoder (AE) are built using fully connected layers, while convolutional neural networks (CNNs) are characterized by alternating groups of convolutional and pooling layers. Recurrent neural networks (RNNs), such as vanilla RNNs, gated recurrent units (GRUs), and long-short-term memory (LSTMs), are based on recurrent layers. For a comprehensive introduction to these neural network architectures, please refer to reference [22]. Depending on the specific problem, these models can be trained using supervised, unsupervised, or semi-supervised approaches. In a deep-supervised learning approach, DNN, CNN, RNN, LSTM, and GRU are the different DL techniques

<table><caption>TABLE II
LIST OF KEY ACRONYMS</caption><thead><tr><td>Acronyms</td><td>Description</td></tr></thead><tbody><tr><td>IoT</td><td>Internet of Things</td></tr><tr><td>IIoT</td><td>Intelligent IoT</td></tr><tr><td>FL</td><td>Federated Learning</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>DL</td><td>Deep Learning</td></tr><tr><td>FTL</td><td>Federated Transfer Learning</td></tr><tr><td>CNN</td><td>Convolutional Neural Networks</td></tr><tr><td>RNN</td><td>Recurrent Neural Network</td></tr><tr><td>GRU</td><td>Gated Recurrent Unit</td></tr><tr><td>BiGRU</td><td>Bidirectional GRU</td></tr><tr><td>LSTM</td><td>Long Short Term Memory</td></tr><tr><td>BiLSTM</td><td>Bidirectional LSTM</td></tr><tr><td>DNN</td><td>Deep Neural Network</td></tr><tr><td>GAN</td><td>Generative Adversarial Network</td></tr><tr><td>TL</td><td>Transfer Learning</td></tr><tr><td>GNN</td><td>Graph Neural Network</td></tr><tr><td>HFL</td><td>Horizontal FL</td></tr><tr><td>VFL</td><td>Vertical FL</td></tr><tr><td>DRL</td><td>Deep RL</td></tr><tr><td>MLP</td><td>Multi-layer Perceptron</td></tr><tr><td>COVID-19</td><td>CoronaVirus Infection Disease</td></tr><tr><td>EHR</td><td>Electronic Health Records</td></tr><tr><td>RL</td><td>Reinforcement Learning</td></tr><tr><td>DQN</td><td>Deep Q-network</td></tr><tr><td>MARL</td><td>Multi-Agent Reinforcement Learning</td></tr><tr><td>AE</td><td>AutoEncoder</td></tr><tr><td>VAE</td><td>Variational AE</td></tr><tr><td>SVM</td><td>Support Vector Machines</td></tr><tr><td>KNN</td><td>K-Nearest Neighbors</td></tr><tr><td>DT</td><td>Decision Tree</td></tr><tr><td>RF</td><td>Random Forest</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>HAR</td><td>Human Activity Recognition</td></tr><tr><td>IoMT</td><td>Internet of Medical Things</td></tr><tr><td>CPS</td><td>Cyber-Physical System</td></tr><tr><td>IID</td><td>Independent and Identically Distributed</td></tr><tr><td>AAL</td><td>Ambient Assisted Living</td></tr><tr><td>AR</td><td>Augmented Reality</td></tr><tr><td>IDS</td><td>Intrusion Detection Systems</td></tr><tr><td>IPS</td><td>Intrusion Preventive Systems</td></tr><tr><td>WSN</td><td>Wireless Sensor Network</td></tr><tr><td>HE</td><td>Homomorphic Encryption</td></tr><tr><td>DP</td><td>Differential Privacy</td></tr><tr><td>SMPC</td><td>Secure Multi-party Computation</td></tr><tr><td>RFID</td><td>Radio Frequency Identification</td></tr><tr><td>OBU</td><td>OnBoard Unit</td></tr><tr><td>GDPR</td><td>General Data Protection Regulation</td></tr><tr><td>DoS</td><td>Denial-of-Service</td></tr><tr><td>DDoS</td><td>Distributed DoS</td></tr><tr><td>DBN</td><td>Deep Belief Networks</td></tr><tr><td>NN</td><td>Neural Network</td></tr><tr><td>UAV</td><td>Unmanned Aerial Vehicle</td></tr><tr><td>RSU</td><td>Road Side Unit</td></tr><tr><td>SNN</td><td>Spiking Neural Networks</td></tr><tr><td>EV</td><td>Electric Vehicle</td></tr><tr><td>BS</td><td>Base Station</td></tr><tr><td>VANET</td><td>Vehicular ad hoc Network</td></tr><tr><td>IBE</td><td>Identity-Based Encryption</td></tr></tbody></table>

mostly used in IoT applications. With deep semi-/unsupervised learning, generative adversarial networks (GAN), and AE are also one of the most popular models.

To achieve ML-based intelligence in IoT systems, a wide range of techniques are used, such as supervised learning, unsupervised learning, semi-supervised learning, and RL
<figure><img src="image_3.png" /><figcaption>Fig. 2. AI, ML, DL, and DRL relationships and their main models.</figcaption></figure>

(Fig. 2). *Supervised learning* is the most used approach of ML process in IoT systems. With this learning approach, the models operate by assigning an observation to a predefined class based on a set of observed features related to that observation. Instead, *unsupervised learning* aims to discover relationships between input data without prior knowledge of the output. In particular, unsupervised learning seeks to categorize input data into distinct clusters (i.e., groups) by analyzing the similarities between the input data. Finally, *semi-supervised learning* combines supervised and unsupervised learning and incorporates labeled and unlabeled data to train the model. As labeling the data can be challenging and time-consuming, semi-supervised learning aims to mitigate these issues by utilizing a few labeled examples alongside a large collection of unlabeled data. Such a learning approach is particularly suitable when large amounts of unlabeled data are available, as is often the case with network traffic. Consequently, in recent years, there has been a growing interest in semi-supervised learning within IoT systems. On the other hand, the main idea of RL was inspired by biological learning systems. It is different from supervised and unsupervised learning, where, instead of trying to find a pattern or learning from a training set of labeled data, the only data source for RL is the feedback that a software agent receives from its environment after performing an action [23].

## B. Reinforcement Learning-Based IIoT

RL is a subfield of ML that emphasizes sequential decision making under uncertain conditions. Unlike other learning approaches, RL does not require predefined target outcomes; its primary objective is to enable an agent to maximize its cumulative reward by taking a series of actions in response to a dynamic environment. RL has been successfully applied to a wide range of IoT problems, including robotics, autonomous vehicles, recommendation systems, and optimization of network resources [11], [24]. In RL, an agent

interacts with its environment by performing actions, receiving feedback in the form of rewards or penalties, and updating its knowledge based on these experiences [23]. The agent's goal is to learn an optimal strategy for selecting actions that maximize the expected cumulative reward over time. This learning process typically involves exploring the environment to gather new information and taking advantage of the acquired knowledge to make better decisions. The key formula in RL is the update rule for Q-learning, given by

$$Q(s, a) \leftarrow Q(s, a) + \alpha \left[ r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right],$$

where $Q(s, a)$ is the value of taking action $a$ in state $s$, $r$ is the reward, $\gamma$ is the discount factor and $\alpha$ is the learning rate.

In the context of highly dynamic IoT networks, the development of decision-making strategies learned by agents is a promising research direction. The adaptive capabilities of these agents allow them to respond to changes effectively. Moreover, in an IIoT ecosystem, various devices often need to work together to achieve a common goal, and RL provides a framework for these multi-agent systems. It allows devices to learn, cooperate, and perform tasks more efficiently. Additionally, RL can also play a role in edge computing, a critical aspect of IIoT, where computations are performed closer to where the data are generated. RL can help optimize data processing and management tasks on the edge, reducing latency and bandwidth requirements. As a result, within the IIoT ecosystem, RL can have essential capabilities by enabling more efficient, adaptable, and autonomous systems, improving the overall effectiveness and utility of IIoT applications [25].

## C. Federated Learning-Based IIoT

Innovative AI solutions that can withstand both data delays and data sensitivity are required for IoT applications so that they can operate locally without sending data to a centralized organization [26], [27]. Due to the distributed and
privacy-enhancing features of FL, some IIoT applications have undergone significant changes since their introduction [28], FL is a decentralized learning strategy in which training data that cannot be shared with third parties are distributed across devices [29]. In particular, FL aims to build a global model that minimizes the average loss function on local datasets. FL is one of the most effective solutions for creating distributed IoT systems due to recent improvements in mobile technology and growing concerns about privacy leakage. To ensure that user data remain at the point of origin and are not shared directly with third parties, FL delegated AI functions, such as data training, to the edge of the network where IoT devices are located. In terms of network resource conservation and privacy enhancement, this strategy enables cooperative training of a shared global model, offering benefits to both network operators and IoT consumers.

As a result, FL becomes a solid substitute for traditional centralized models, accelerating the implementation of IoT services and applications on a larger scale. IoT devices and the aggregated server are the two key parts of the FL concept in IoT networks, and training can take place in a centralized or decentralized manner. One of the most commonly utilized FL topologies in FL-IoT systems is centralized FL. A weighted averaging approach, such as Federated Averaging (FedAvg) [27], [30], is used to aggregate the models learned from each IoT device, which trains the model locally using its private data. The formula for FedAvg can be mathematically represented as

$$ \theta = \sum_{k=1}^{K} \frac{n_k}{n} \theta_k, $$

where $\theta$ is the global model parameters, $K$ is the number of local models, $n_k$ is the number of data points in the $k$-th local dataset, $n$ is the total number of data points, and $\theta_k$ are the parameters of the $k$-th local model.

Using the received global model, the training is performed on the consensus model that the server has returned. In a decentralized or peer-to-peer FL system, the process of model aggregation does not rely on a central server. As an alternative, each device shares its trained models with some or all of its peers, and each one is responsible for its aggregate.

Additionally, FL may be separated into Horizontal FL (HFL), Vertical FL (VFL), and Federated Transfer Learning (FTL), depending on how the client data are dispersed. IoT devices use HFL, which uses multiple sample spaces while maintaining the same feature space. Customers may use the same model for in-person training due to the characteristics of constant data. An example of HFL is intrusion detection [31] in the intelligent industry. Representative and low-dimensional features are learned by training an AE model on each device (using unlabeled local data). Then, a cloud server uses FedAvg to combine these models into a global AE. The cloud server then builds a supervised neural network by including fully connected layers on top of the global encoder and trains the resultant model using data that have been tagged and are readily available online. In contrast to HFL, the learning of shared models is addressed by VFL on devices with the same

sample space but several data feature spaces [32]. A smart city's shared learning model, which includes e-commerce businesses and financial institutions, is an illustration of VFL in IIoT applications. Different data characteristics that serve city clients inside a smart city, an e-commerce business, and a bank can participate in a VFL process to collaboratively train a model utilizing their datasets. These datasets might contain past user payments from online retailers and bank user account balances. This approach enables VFL to predict the best-tailored loans for each consumer based on their online buying habits. Last but not least, the FTL combines the FL and transfers learning ideas to [33]. By adding additional learning clients who have datasets with various sample and feature spaces, it seeks to increase the sample space in the VFL architecture. FTL can help detect disease in IoT networks by promoting cooperation between various nations with numerous hospitals that have unique patients (sample space) and a variety of diagnostic procedures (feature space). By doing this, the FTL may enhance the output of the shared model, increasing diagnostic precision.

## D. Visions of the Use of AI in IoT

IIoT represents the fusion of advanced technologies, such as ML, DL, RL, FL, data analytics, cloud, and edge computing, to create smart devices that communicate with each other through the Internet. By integrating these technologies, vast amounts of data can be harnessed for big data analytics, leading to valuable insights and scientific knowledge. In particular, ML and DL models can be used to create data analytics tools that can handle information collected from a variety of IoT devices, including sensors, smartphones, computers, and radio frequency identification (RFID) devices. These models are capable of handling a wide range of IoT data types, including enormous data volumes (data streaming from millions of sensing devices) and many modalities (images, time series data, video and text) [34], [35]. In an unmanned aerial vehicle (UAV)-assisted cellular offloading situation, for instance, the study [36] investigated the combined optimization of the UAV trajectory design and power allocation. By coordinating many devices to execute model training in IoT devices, FL enables IIoT to achieve better scalability and privacy protection while guaranteeing that data stay in the location where it was created. Table III provides an overview of how the different ML, DL, DRL, and FL paradigms can be used in IIoT applications.

Although the integration of AI and IoT allows IoT devices not only to collect and communicate data, but also to learn from it, and to make decisions based on those data, security and privacy remain critical concerns in the IIoT landscape. With devices collecting, processing, and storing enormous volumes of data, a significant portion of which can be sensitive or personal, there is a mandatory need to develop efficient privacy preservation methods. In IIoT, devices are continuously communicating with each other and with the cloud, requiring secure communication channels to guarantee confidentiality and integrity. Moreover, AI models deployed in IIoT can become targets for adversarial attacks, aimed at manipulating the model's behavior. Addressing these challenges requires
<table><caption>TABLE III
USAGE OF AI PARADIGMS IN IIoT APPLICATIONS</caption><thead><tr><td>Paradigm</td><td>Characteristics</td><td>Usage within IIoT</td></tr></thead><tbody><tr><td>Shallow ML</td><td>- Fast training.<br/>- Perform well on small datasets.<br/>- Tend to be more interpretable.<br/>- Process data in real-time.</td><td>- Detect faults or anomalies in manufacturing processes.<br/>- Predict energy consumption patterns of IoT devices.</td></tr><tr><td>DL models</td><td>- Learn hierarchical representations of data.<br/>- Capture intricate patterns and relationships in different types of data.<br/>- Process vast amounts of data.<br/>- Outperform shallow ML-based models in many complex IoT applications.</td><td>- Analyze visual data from surveillance cameras or drones.<br/>- Capture temporal and spatial dependencies for predictive maintenance.<br/>- Integrate data from multiple sensors in IoT systems such as vision, audio, and sensor data.</td></tr><tr><td>DRL</td><td>- Learns directly from interactions with the environment.<br/>- Focuses on sequential decision-making tasks.<br/>- Learn efficiently from limited interaction samples with the environment.<br/>- Leverages transfer learning.</td><td>- Empowers autonomous systems in IoT.<br/>- Optimizes resource allocation in IoT networks.<br/>- Optimizes irrigation schedules.</td></tr><tr><td>Federated Learning</td><td>- Ensures that sensitive or private data does not communicated.<br/>- Operates on decentralized data sources.<br/>- Promotes collaborative learning among the decentralized devices or servers.<br/>- Accommodates a large number of devices participating in the training process.</td><td>- Allows IoT devices to collaboratively train models for anomaly detection.<br/>- Enables edge devices to collaboratively train models.<br/>- Supports traffic prediction in IoT-enabled systems.</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 3. IIoT application domains.</figcaption></figure>

multidisciplinary research, including areas such as cryptography, ML/DL-based models [37], hardware design, and network security. It is also worth noting that any solution needs to take into account the specific constraints and requirements of IIoT, such as resource limitations, the need for real-time operation, or the scalability to large numbers of devices [38].

### III. IIoT APPLICATIONS

As stated in the previous sections, the integration of AI/ML into the IoT has favored a new plethora of novel applications. This section provides an extensive discussion of such representative IIoT applications, including smart healthcare, smart cities, smart transportation, and smart industries, along with applied use cases, as shown in Fig. 3.

#### A. IIoT-Based Smart Healthcare

Recently, patient monitoring has been a crucial concern; therefore, healthcare is projected to be the dominant application of IIoT systems [39]. Advances in IIoT-enabled systems may lead to significant benefits in healthcare, allowing medical personnel to identify anomalies earlier, monitor and prognosis critical diseases, and reduce potential risks for critically ill patients as much as possible. The ubiquity of wearable, lifestyle and assistive IoT devices helps to collect massive amounts of data to train and improve the performance of ML-based models and, in turn, provide better healthcare

services. For instance, the study in [40] used heart rate variability for the prediction of arrhythmias with the KNN classifier, which led to a high accuracy of 97% in detecting cardiac arrhythmias in real time. Here, we focus on examining the functions of IIoT in healthcare care using two use scenarios, namely COVID-19 detection and ambient assisted living (AAL).

1) Ambient Assisted Living (AAL): AAL technologies are designed to support people without obstruction in their daily activities, enhancing their quality of life, safety, and independence. IIoT can play an important role in this area by monitoring health parameters, creating a more comfortable and manageable living environment, and reducing feelings of isolation and loneliness. Sleep monitoring is one of the services associated with AAL as appropriate sleep is an integral part of a healthy lifestyle. The recent development of smartphone applications and sleep sensing devices, such as electroencephalograms (EEG) and electrooculograms (EOG), has increased the analysis of sleep patterns. To detect changes in sleep patterns, the work in [41] proposed an accurate deep hybrid model followed by a K-Medoid algorithm. It combines both Deep Belief Networks (DBNs) for unsupervised feature learning and LSTM to improve classification performance and learn long-range dependencies in time series data. The features used to train these models were total sleep duration, sleep efficiency, hypnagogic time, number of awakenings, self-evaluations of sleep, and physical condition. Longitudinal
sleep sequences consisting of daily sleep types were clustered using a K-Medoid algorithm to find the patterns of each sleep sequence cluster. Another approach to monitoring posture during sleep by using a pressure sensor mattress that records pressure in different areas of the mattress has been proposed in [42]. The proposition consists of two phases: in the first phase, the sensor data are pre-processed using Principal Component Analysis (PCA) for feature extraction and dimensionality reduction, whereas the second phase uses the extracted features to train and then the SVM model to classify the data into prone/supine, left, and correct lateral positions.

Although PCA can learn and extract relevant features, its linear combination of features does not have sufficient capability to model complex non-linear dependencies. To overcome these issues, CNN models have represented important research on AAL trends. This is because of their capability to extract relevant features and classify large-scale images. It has certain advantages over traditional shallow learning techniques for image treatment and analysis, particularly in AAL and Human Activity Recognition (HAR). The first CNN-based approach for HAR was proposed in [43]. It automatically learns and extracts human activity features without any domain knowledge (such as activities in the kitchen, jogging, walking, etc.). The experimental results showed that the CNN-based approach can outperform the PCA method. Similarly, the authors of [44] investigated the efficiency of a CNN by comparing its performance with state-of-the-art methods using benchmark datasets. This study shows that it serves as a competitive tool for feature learning and classification for HAR applications. Furthermore, an end-to-end CNN model was proposed that showed good results in predicting three movements of the arm performed in daily activities in [45]. Despite the performance of the CNN model, its complex architecture due to the growing number of hyperparameters may increase computational costs and make it unsuitable for AAL applications with strict latency constraints. Therefore, a model that can balance the trade-off between accuracy and computational cost is required. To do so, the authors of [46] proposed, for the first time, a computation-efficient CNN by replacing conventional convolutions with the conditionally parameterized convolution algorithm (CondConv). Using WISDM, PAMAP2, UNIMIB-SHAR, and OPPORTUNITY datasets, the proposed solution always performed better than its counterpart without CondConv without compromising inference speed.

However, CNN-based models sometimes fail to extract temporal features, and capturing these temporal dynamics is fundamental for successful AAL applications. To overcome these limitations, CNN-LSTMs have been proposed as a combination of CNN and LSTM layers. In this context, the authors of [47] proposed a DL framework composed of convolutional and LSTM models, called DeepConvLSTM. The convolutional layers provide abstract representations of the input sensor data, the LSTM models, and the temporal dynamics of the activation of the extracted features. Performance evaluation against CNN baseline shows that DeepConvLSTM achieves a higher F1 score with the OPPORTUNITY dataset.

Processing of data collected from all users by a central body is often necessary for these systems to operate well. Data in healthcare systems are extremely sensitive and governed by laws such as the United States Health Insurance Portability and Accountability Act (HIPPA) compared to other IIoT applications [48]. As a result, operators have trouble collecting a lot of data, especially if patients and hospitals are reluctant to provide their sensitive information. The latency and storage resources that arise when patient-sensitive data need to be collected in the cloud for model training are the next challenge for standard ML models. As a consequence, FL has been used as a promising alternative to deploy IIoT, as it provides intelligence with privacy awareness and without the need to share end-user data. Several research efforts have been devoted to the use of FL for AAL, enabling intelligent solutions in healthcare systems and protecting privacy-sensitive medical data [49]. For instance, the authors of [50] proposed an FL-based approach to the predictor of HAR in a smart environment in a realistic scenario. The designed approach uses the LSTM model and trains it in three scenarios: local, centralized, and federated. Using the CASAS dataset, the evaluation results show that the FL approach can provide competitive results compared to centralized learning.

Labeling data for FL-based models is frequently challenging and time-consuming because FL model training typically takes place on equipment that is out of reach for humans. Therefore, to scale up an HAR system over a large number of devices, researchers have begun to reformulate FL as a semi-supervised issue [51]. According to the semi-supervised FL for HAR proposed in the work in [52], clients would locally train an AE model using their unlabeled data, and the server would then include the local AE models that were created into the pipeline of the supervised learning process. Their experimental findings demonstrate that HAR with semi-supervised FL may reach accuracy on par with supervised FL without being unaffected by non-independent and identically distributed (non-IID) data. Similarly, the work in [53] combines Active Learning (AL) with label propagation, dubbed textttFedHAR, to federated semi-automatically annotate the end-user data. The simulation results using the two well-known datasets MobiAct and WISDM show that the labeled data produced by AL offer pertinent informative data and, consequently, allow FedHAR to achieve competitive performance with a fully supervised FL model (FedAvg).

Furthermore, to reduce the impact of non-IID data, transfer learning techniques have been introduced on the client side to improve personalization. For example, [54] investigates the emerging personalized FL method by proposing PerFit framework that can mitigate the negative effects caused by statistical heterogeneities. The authors investigated the performance of two customized FL-based solutions: FTL and federated distillation (FD) using the PerFit framework. With FTL, each client adjusts the model they downloaded from the cloud server using their unique data, but with FD, each client may create their model based on their unique needs. The experimental findings demonstrate that customized FL can reduce the performance loss caused by non-IID data and that both FTL and FD are capable of collecting user data.
Additionally, using extremely small Internet of Medical Things (IoMT) devices, also known as nanodevices, may be necessary to distribute healthcare services across larger regions. These units, called straggler clients, may respond very slowly or may not be able to complete the anticipated number of local iterations. There are a few ways that can prevent choosing these customers throughout the training process. However, dropping stragglers can diminish the number of active clients and, consequently, the performance of the model [49]. To solve this problem, the authors of [55] introduced a lightweight Federated Proximal, Activity, and Resource-Aware model for a resource-constrained IoT environment, called FedPARL. To allow the client to accommodate the reduced model sizes in the first layer, FedPARL conducts sample-based model pruning on the server. To choose competent and reliable clients for training, the second layer of FedPARL additionally looks at previous actions and resource availability (CPU, memory, battery life, and data volume). By allocating local epochs in the third layer following the client's resource availability, partial work was made possible. The results show that FedPARL achieves an improved stable accuracy compared to the FedAvg and FedProx approaches. On the other hand, the authors of [56] proposed a heterogeneous federated stacking model to monitor patient activities based on classification models, called FedStack. This model can process a variety of client-model architectures and group the local models into a global robust model, which in turn can reduce the impact of the stragglers' clients on the classification performance. To do so, three different DL-based models (MLP, CNN, and LSTM) were used and individually trained on each data set. Then, the model predictions were stacked homogeneously and heterogeneously and then passed to the global model. The results show that the proposed FedStack achieves better accuracy than several baseline models for HAR.

2) **COVID-19 Detection:** Recently, the COVID-19 pandemic has reached 214 countries and regions across the world, profoundly affecting daily life in those areas. The potential of IIoT to support the fight against COVID-19 has been investigated in [49], [57]. The IIoT can help with timely quarantine and medical treatment. The use of ML/DL models with computed tomography (CT) images for COVID-19 detection was considered in several works [58]. For example, in [59] an early detection of COVID-19 based on SVM has been proposed. Features were extracted through several features extraction methods (Grey Level Co-occurrence Matrix (GLCM), Local Directional Pattern (LDP), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), and Discrete Wavelet Transform (DWT)) followed by SVM as a classifier. Despite the performance of the SVM, it cannot scale very well. To overcome the scalability problem of the shallow model CNN-based models have been used for chest CT image treatment and processing [60]. In this context, to classify COVID-positive and COVID-negative, the work in [61] used different CNN models, which are pre-trained models ResNet50, ResNet101, ResNet152, InceptionV3, and InceptionResNetV2 with three binary data sets that include X-ray images of patients with normal (healthy), COVID-19,

bacterial, and viral pneumonia. The result is promising, especially since ResNet50 and ResNet101 have achieved performance with 96.1%. It should be noted that the COVID-19 dataset [62] and Kaggle's Chest X-ray Images (Pneumonia) are also used to form the data set in this study.

Although the efficiency of the above models was observed, the similarity of different datasets was not considered. At the same time, making knowledge transfer inter-domains possibly leads to a more precise and personalized model, and one way to achieve this is via TL. TL, which is widely used in the field of DL, may be a promising solution for COVID-19 detection. It enables the detection of various COVID-19 using small medical image datasets, helps to avoid learning from scratch, and, in turn, reduces energy consumption. For instance, the study in [63] suggested that transfer learning can extract relevant features related to COVID-19 disease and, in turn, improve detection performance. In a similar work [64], a deep TL model is used to classify COVID-19 infected patients and showed that DTL is a useful approach to the classification of COVID-19. In addition, FL has also been used to detect positive cases by training models from isolated medical institutions. Each medical facility that uses FL participates in training using its own local COVID-19 images, such as X-ray and CT scans, and only model parameters are exchanged-sensitive user data are not necessary. In this regard, [65] proposes a multi-institutional collaborative FL framework for COVID-19 identification. Similarly, [66] has suggested a 5G-enabled architecture for COVID-19 diagnostics that communicates with several hospitals through FL while protecting privacy. The authors of [67] also suggested a dynamic fusion-based FL technique to increase the performance of the model and the effectiveness of communication to identify patients infected with COVID-19. In addition to the aforementioned applications, employ DL, such as the LSTM model in real-time to anticipate the dynamics of the COVID-19 epidemic to improve health and policy initiatives [68], [69].

## B. IIoT-Based Smart City

IIoT plays a crucial role in smart city development, as it can facilitate data collection, exchange, and analysis, leading to improved services, efficiency, and quality of life. There are several key domains in which IIoT can offer useful services for smart cities, including smart grid and water management.

1) **IIoT for Smart Grids:** With the rapid growth of the population and the increase in the number of industries, traditional grids for reducing power consumption and forecasting demand have become inefficient. To address these issues, an intelligent infrastructure is needed for the successful processing of large amounts of data. Over the past decade, we have seen a shift toward smart grids, which has replaced traditional power grids largely [70]. Due to recent advances in IIoT, smart grids can intelligently learn patterns and ensure intelligent and automated power grids. Smart grids are among the most prominent applications of the IIoT, with their convergence unveiling considerable potential for modernizing electric power systems. They established a fully automated and intelligent energy delivery network by integrating all
users connected to them [71]. Beyond their role in energy networks, smart grids play a crucial role in the future of renewable energy systems. They improve the reliability of power supplies, help reduce CO₂ emissions, and promote the adoption of green energy sources [72]. Therefore, with the integration of smart grids and IIoT infrastructure, end users can analyze and monitor their daily energy consumption patterns. This enables greater control and management of energy use, offering the potential to schedule energy use through a simple mobile application.

Recently, ML has also been applied to energy management in smart grids. An example is presented in [73] where an ML-based energy management framework was proposed to mitigate energy management problems on the demand side. This approach integrated Gaussian process regression (GPR) with ML to develop an efficient energy management model (EMM). Performance parameters calculated from an optimized model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) were fed into the ML-based GPR system for training. This adaptive framework of service level agreements (SLAs) between energy consumers and the grid benefits all stakeholders involved. Furthering the integration of ML in smart grid systems, [74] introduced a smart grid system augmented with a Cyber-Physical System (CPS) model. They proposed a multidirectional LSTM, known as the (MLSTM) model, which is specifically designed to predict the stability of a smart grid network. The experimental results validate that the MLSTM approach outperforms other conventional ML methods in terms of performance, emphasizing the power of advanced ML techniques in this domain.

Furthermore, the integration of FL with smart grids presents a novel approach that benefits from the power of decentralized machine learning. However, during FL training rounds, communication between clients and the server may fail due to the time-varying reliability properties of links in a wireless network of smart grids. Such communication failures not only hinder the convergence rate of the model, but also lead to resource wastage, including energy expended on unsuccessful local training. Recognizing these challenges, [75] introduced a dynamic FL problem in a grid mobile edge computing (GMEC) environment, considering the high dynamism of link reliability. To mitigate communication failures between industrial clients and the server, the authors proposed a delay deadline-constrained FL framework. This structure aims to prevent excessively long training delays by formulating a dynamic client selection problem, maximizing the computing utility while also minimizing the communication latency during the FL process. At the same time, several other FL algorithms have been formulated to address similar challenges. For example, [76] presented a framework for power learning in IoT networks comprising electricity providers and users. The distinctive communication model they developed was based on the FL process. This model seeks to balance the trade-offs between resource consumption, user utility, and local differential privacy, thereby presenting a comprehensive and holistic approach to managing smart grid networks using FL. In this context, the study in [77] proposed an FL approach for the secure and efficient sharing of personal

energy data in smart grids with edge-cloud collaboration. This hierarchical FL framework promotes the analysis of energy data that preserves privacy and is communication efficient within smart grids. This approach considered non-IID data from heterogeneous users, which inspired the development of a local data evaluation model for cost modeling and two optimization problems specifically tailored for energy service providers and energy data owners. In the final phase, the study introduced a DRL-based incentive algorithm designed to manage multidimensional user private information and vast state spaces. This strategy facilitates the determination of optimal pricing strategies for energy data providers and optimal training strategies for energy data owners. Similarly, the research conducted by [78] proposed a cloud-edge FL-based strategy that involved clustering of the FL-based energy demand predictor system. This innovative system groups clients with similar attributes, which, in turn, allows the aggregation of model updates from clients within the same cluster. This novel approach leverages the synergy between advanced ML techniques and the capabilities of smart grids, indicating promising new directions for research and application in this field.

## 2) IIoT for Underwater
The demand for water from industries, factories and mining continues to grow with increasing urbanization. Simultaneously, there is an increase in wastewater disposal without appropriate treatment from natural sources, which also pollutes unpolluted water [79]. The implementation of smart water management mechanisms for effective distribution, conservation, and maintenance of water quality is now more crucial than ever. For example, in agriculture, significant challenges are associated with access to water, efficient use, and the incorporation of sustainable practices for water conservation and harvesting.

Technological progress in the field of industrial IoT presents an exciting prospect of significant improvements in underwater exploration, environmental monitoring, and industrial processes [80]. These technologies can play an instrumental role in creating an intelligent water management system (illustrated in Fig. 4), which comprehensively covers aspects such as wastewater management, irrigation, rainwater harvesting, water reuse, and sustainable sourcing from natural resources. The study carried out in Tunisia as a joint project with the Water Production and Management of Water capitalized on the integration of AI and IoT technologies to improve productivity by reducing wasteful consumption and improving user access to accurate and timely information [81]. The study introduced a novel technique to monitor water consumption using an optical character recognition device (OCR) in conjunction with the YoLo 4 ML model, focusing on smart cities' paradigms. Meanwhile, other researchers have focused on the simulation

<figure><img src="image_5.png" /><figcaption>Fig. 4. IIoT for water management parameters.</figcaption></figure>
of water quality and levels. For instance, a study in the Nakdong River basin of South Korea conducted such simulations, taking into account parameters such as organic carbon, phosphorus, and nitrogen content [82]. Another study explored the application of DL, proposing integrated automatic detection models that used U-net and CNN to identify temporal resolution and high spatial imagery systems that are the key to mapping pivotal irrigation systems in the center [83]. These advancements underscore the transformative power of IIoT in redefining the proposed approach toward sustainable and intelligent water management.

On the other hand, increasing water pollution around the world is an endangering factor for water quality. To predict water quality, the work in [84] used a BiLSTM with time-series data. The model used monthly data collections from quality reports for six years (2013-2019) on the Yamuna River, New Delhi. The BiLSTM model was unique in its approach, as it focused not only on training, but also on missing value imputation, thereby ensuring a comprehensive analysis of the available data. In the same context, the authors of [85] demonstrated that the usage of auto DL techniques for the determination of water quality gives better results. Auto DL, one of the most recent and promising technologies, enables straightforward interpretation and model creation with possibly minimal coding requirements. Its application in this domain yielded favorable results, further validating the potential of Auto DL in environmental analysis. Conventional Internet of Underwater Things (IoUT) systems frequently employ centralized learning to ensure efficiency, reliability, and timeliness. Recent research has emphasized the significance of privacy and security in mission-critical IoUT frameworks [86]. FL as a secure, decentralized ML system can be used to address the IoUT challenges. The study outlines FL's potential applicability in IoUT, highlighting its issues, unresolved topics, and future research directions. In this context, the authors of [87] proposed a federated meta-learning enhanced acoustic radio cooperative framework for the Ocean of Things, which takes advantage of the data distributed on the surface nodes. This continuous exploration of advanced FL techniques in IoUT projects a promising future for this field.

### C. IIoT-Based Smart Transportation

The recent development of IIoT-based smart transportation systems improves the safety and sustainability of urban mobility and therefore makes daily life more convenient and efficient. It can facilitate smart transportation by treating and analyzing the data collected from a wide range of sensors to make decisions in real time. Existing researchers have made great efforts to investigate different aspects of applying IIoT in smart transportation development, including autonomous and electric vehicles, as well as traffic management systems.

1. **Autonomous and Electric Vehicles:** Intelligent and autonomous driving has become possible with the help of IIoT systems. Traffic sign recognition is a crucial task in advanced driver assistance and autonomous systems. Manual localization and recognition of traffic signs can be extremely

time-consuming when applied to thousands of kilometers of roads. Therefore, the automatic and smart detection and recognition of traffic signs play a pivotal role in smart transportation. In this context, IIoT is used to support vehicles in autonomously obeying traffic rules by correctly recognizing traffic signs [88].

The following aspects highlight IIoT's impact on traffic sign detection and recognition:

* **IIoT for Traffic Sign Detection and Recognition:** Shallow models, such as decision trees and SVM classifiers, are widely used for the recognition of traffic signs due to their ease of training and updating. These models achieved good results [89], [90]. Using feature learning based on real examples can help easily adapt and capture a high degree of variability in the appearance of a large number of traffic signs. The authors of [91] use the Mask Region-CNN (R-CNN) model for large-scale detection and recognition of traffic signs through automated end-to-end learning. The simulation results showed excellent performance in the localization and recognition of traffic-sign instances. Based on [91], the authors of [92] added an attention network to the fast R-CNN model to extract more relevant features from the input data. Another traffic sign recognition system is presented in [93]. The authors used multiple spatial transformers and extensively studied their impact on network configurations within the CNN model. The use of DL-based models to identify traffic signs in real-time with limited equipment resources was proposed in [94]. The authors focused on reducing the computational cost of the model, making it suitable for IIoT scenarios. To do so, a knowledge distillation-based model is proposed, in which the shallow student model is trained through the softened output of the teacher model on the target datasets. The performance of the proposed model demonstrates that a lightweight network can reduce the number of redundant parameters while maintaining comparable accuracy. Similar to the healthcare service, collecting traffic sign data in the cloud presents serious privacy leakage risks owing to the inclusion of location privacy information. To preserve the privacy of traffic sign data with limited computing resources, the work in [95] proposed FedSNN. Without disclosing any personal information, the authors employed FL to carry out cooperative training for accurate recognition models. They also proposed Spiking Neural Networks (SNNs), a third generation of neural networks, to further save energy and processing resources. As shown in Fig. 5, the networked vehicle trains the SNN model using the local traffic sign dataset during each communication round. The trained model is subsequently delivered to a Road Side Unit (RSU), where it is utilized for global aggregation, after data encoding.

* **Energy consumption of electric vehicles:** According to the International Energy Agency [96], the number of electric vehicles (EV) on the road will increase tremendously by more than 4000% in 2030. This trend will lead to an explosion in energy consumption, and providing convenient charging services is of critical importance to
<figure><img src="image_6.png" /><figcaption>Fig. 5. Overview of FedSNN for road traffic sign recognition [95].</figcaption></figure>

enhance the user experience. As a result, many IIoT-based solutions have been proposed to predict the energy demand for EVs. In this context, the authors of [97] introduced a mobile application that can estimate energy availability and/or expected charging finishing time at the charging station using the KNN algorithm. The application collects historical data on the energy consumed and charging duration of connected electric vehicles on a remote server, which is then used to train KNN for the prediction task. In a different approach, [98] proposed a Federated Energy Demand Learning (FEDL) method for predicting energy demand. To increase the efficacy of the FEDL method, the authors introduced a clustering technique that groups charging stations into groups before performing the learning process. The effectiveness of the system was tested using real data from charging stations in Dundee City, the United Kingdom, collected between 2017 and 2018. The results showcased improved prediction accuracy while maintaining a good communication overhead, thus demonstrating the potential of this approach. Furthermore, the study in [99] adopted the RL-based algorithm to solve the daily energy that should be added to the batteries of electric vehicles. In particular, the authors cast the EV charging problem as a Markov decision process for choosing the amount of energy to be charged, to reduce the long-term cost of charging for an individual plug-in EV based on the current known day-ahead and following predicted electricity prices. Also, a novel approach has been proposed to model the behavior of plug-in electric vehicles (PEV) in the energy market has been proposed in [100]. The authors introduced a clustering technique to group PEVs that exhibit similar behavior patterns. Subsequently, an LSTM model is assigned to each cluster, which is tasked with capturing and forecasting the unique behavior of each cluster. The use of clustering techniques helps decrease the complexity of the model, improving its performance in predicting PEV behavior. Performance evaluations showcased the effectiveness of the proposed method in accurately modeling and predicting PEV behavior in the energy market, outperforming traditional forecasting methods. Furthermore, the authors of [101] aimed to increase the proportion of vehicles charged with

insufficient energy and to reduce the charging costs of these vehicles. To achieve this, they proposed an efficient placement strategy for idle mobile charging stations, termed FL-PDMIM. This innovative approach indicates the continued search for more effective solutions in the management of energy consumption for electric vehicles. On the other hand, vehicle emissions have become a major and costly problem in many countries with the growth of the population and motor vehicles. To solve this problem, eco-driving is one such technique, which aims to reduce vehicle fuel consumption and emissions by providing proper guidance. In this context, the authors of [102] used traditional (non-deep) Q learning to minimize CO₂ emission at signalized intersections. Similarly, this study [103] used a deep RL algorithm to learn eco-driving strategies in an urban environment, where the agent only received minimal connectivity data and no explicit prediction of the traffic situation. The work in [104] developed a Deep Deterministic Policy Gradient (DDPG) algorithm to “learn” an eco-driving velocity planner for a plug-in hybrid electric vehicle within a model-free approach.

2) Traffic Management Systems: Traffic flow prediction is an important functional component of Intelligent Transportation Systems (ITS). It guarantees a more pleasant driving environment and eliminates the possibility of traffic accidents. It is one of the largest segments within smart transportation, where the adoption of IIoT technologies is considered the most prominent. An enormous amount of spatio-temporal traffic data and vehicle-related information is being produced through devices such as cameras, various sensors, and Global Positioning Systems (GPS) [105]. This data is then transferred to traffic management centers, allowing for timely decision making. Often, techniques such as Autoregressive (AR) and Moving Average (MA) are used to model the time series data, giving rise to models like Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). However, these methods can prove to be insufficient for traffic prediction due to the highly dynamic and non-linear nature of spatio-temporal correlations between different positions. Therefore, this scenario calls for the development and adoption of more sophisticated models that can effectively capture these complex dynamics.

Compared to classical statistical models, data-driven ML-based traffic prediction models can handle high-dimensional data and obtain their nonlinear relationships well. Thus, ML models can be established as strong competitors to classical statistical models and receive tremendous attention in traffic prediction. In this context, the study in [106] proposed an improved KNN model to improve the accuracy of the forecast based on spatiotemporal correlation and achieve multi-step forecasting. Rather than relying on traditional time series, this model represents the traffic state via the spatio-temporal state matrix. The model uses Gaussian weighted Euclidean distance to identify ‘nearest neighbors’ and then applies the Gaussian function to assign weights to each nearest neighbor. Furthermore, Random Forest, a robust ML algorithm, has also
been proposed as a viable solution for traffic prediction [107]. These developments underscore the potential and effectiveness of ML to improve traffic prediction outcomes.

Although the feasibility and reliability of KNN and other shallow models may not perform well due to their limited parameter space in modeling complex IIoT traffic. In other words, the complexity of road traffic predicts future traffic volume a very challenging task and beyond the ability of such models. In this context, DL has been used as an alternative solution to infer information from large datasets and requires very little domain knowledge and engineering by hand [108]. Consequently, significant efforts have been devoted to DL for IIoT-based traffic prediction. Generally, CNN is used to extract the spatial correlation of grid-structured data [109] [110], then Graph Convolutional Network (GCN) [111], diffusion convolutional recurrent neural network (DCRNN) [112], and graph Wavenet models [113] have been proposed as an improved version of the convolution operation to capture spatial correlations in non-Euclidean data and show a more efficient representation of the traffic structure. Also, to reduce the prediction error, the authors of [114] added an attention mechanism to a multi-component graph convolutional network to form an attention-based spatial-temporal graph convolutional network. Another graph-based model called MAN employed attention structures in both spatial and temporal dimensions, enabling a more accurate representation of dynamic spatio-temporal correlations [115].

Moreover, to efficiently capture the temporal along the spatial features in traffic flow, the RNN model and its variants LSTM and GRU are combined with CNN or Graph neural network to extract the temporal correlations. For instance, the authors of [116] proposed a hybrid DL approach, called GLA. Within this GLA framework, the output from the GCN model is fed into an LSTM model to capture the temporal dependencies of traffic flow. In the same direction, the study presented in [117] successfully used LSTM to capture both spatial and temporal components of the traffic data by removing dense kernels with convolutional ones. Furthermore, FL is a practical replacement for conventional centralized ML methods in traffic prediction applications. However, developing efficient traffic prediction while preserving privacy is a promising direction. In this context, [118] introduced a solution that combines GRU and FL to predict traffic flow. They proposed a clustering FedGRU algorithm, which integrates the optimal global model and captures the spatio-temporal correlation without necessitating the collection of traffic flow data in the cloud. This strategic approach emphasizes both efficiency and data privacy. Several innovative FL-based models for traffic prediction have also been introduced, as highlighted in recent works such as [119] and [120].

Currently, some researchers have turned their attention toward energy considerations for resource-constrained RSUs. These units often tend to serve vehicles in closer proximity, a practice that can lead to incomplete services and an impaired user experience. To mitigate this issue, the authors of [121] proposed a Q-learning-based intelligent RSU scheduling system. In this architecture, the Q-learning agent chooses which cars to serve as output after taking into account the

system statuses and vehicle demands as input. Additional fines for unfinished services are introduced into the incentive structure to encourage a better customer experience. The agent saves the transition of each interaction in the replay memory to further increase the efficiency of the data. The Deep Q-Network (DQN) parameters are then updated by picking a mini-batch of independent transitions at random. The suggested method can serve more cars and prevent insufficient service, according to the simulation findings. Building on [121], the multi-agent system (MAS) used in the work in [122] expands the technique to include many RSUs working together to meet user needs and increase throughput. Multi-Agent Reinforcement Learning (MARL) is a technique that RSUs may use to give improved utility over a larger region. Thus, both service availability and overall system performance are enhanced by this method.

## D. IIoT-Based Smart Industry

The term “intelligent industry” refers to the integration of smart technology into production processes, and IIoT is essential in the analysis of large amounts of data produced by industrial machinery and IoT devices. In different production phases, these methodologies allow process modeling, monitoring, prediction, and control [123]. Here, we concentrate on examining the functions of IIoT in robots and Industry 5.0.

1) Industry 5.0: The advent of Industry 1.0 in 1874 marked a significant shift in industrial production. Fig. 6 provides an overview of the evolution of Industry X.0. The advent of Industry 5.0 in 2020 ushered in the concept of smart manufacturing for the future. While the fourth industrial revolution, Industry 4.0, focused primarily on the use of technology to optimize the means of production (with technologies such as AI, robotics, IoT, and cloud computing), Industry 5.0 is expected to place a stronger emphasis on the collaboration between humans and machines. It emphasizes the harmony between humans and machines as well and views machines as partners rather than replacements for human labor. This includes using robotics for tasks that are hazardous to humans or require extreme precision and leaving tasks that need creativity and decision-making skills to humans. As societies become increasingly aware of environmental challenges, Industry 5.0 emphasizes sustainable and environmentally friendly production. This includes optimizing resources, reducing waste, and implementing technologies that reduce the environmental impact of production processes.

Industry 5.0 embodies the integration of human subjectivity and intelligence along with the efficiency, precision, and AI capabilities of machines in industrial production [124]. This fusion highlights the importance of humanistic care and paves the way for the evolution toward a symbiotic ecosystem. A key component of this industrial revolution is IIoT, which can be used to support intelligent systems within the context of Industry 5.0. By facilitating seamless communication and interaction between machines and devices, IIoT plays a critical role in driving the shift towards more intelligent and efficient industries. As an example, an automated material identification using machine vision is introduced in [125]. In this study,
<figure><img src="image_7.png" /><figcaption>Fig. 6. Illustration of Industrial Evolution from Industry 1.0 to Industry 5.0.</figcaption></figure>

multiple classification algorithms - including SVM, DT, RF, Logistic Regression, and KNN - are trained on color features extracted from various materials. The experimental results confirm the effectiveness of these algorithms, as demonstrated by their remarkable accuracy in material classification. The adoption of such advanced methodologies ushers in a new era of efficiency and precision in industrial operations. Another example in which these shallow ML models are applied can be found in [126], where logistic regression and RF classifiers are used for automatic monitoring and analysis of the performance of heating, ventilation, and air conditioning (HVAC) systems. These examples demonstrate the potential and versatility of ML in various aspects of Industry 5.0 applications. Similarly, in this work [127], DT-based models including RF, gradient boosting (GB), and XGBoost are used to predict machine failure. Further, in [128], the authors explore the performance of a variety of fundamental ML models within the purview of smart manufacturing. This evaluation provides valuable insight into the strengths and applicability of different shallow models in the industrial context, highlighting their essential role in the realization of Industry 5.0.

Similar to other IIoT applications, DL algorithms have also been extensively investigated for various applications within Industry 5.0. These applications range from predictive maintenance and quality control to system optimization and automated decision-making, demonstrating the vast potential and adaptability of DL techniques in the context of the fourth industrial revolution. In this context, a DL-based approach has been proposed in [129], where a unique approach that takes advantage of DL has been proposed to manage data analytics tasks. This approach uses an open-source framework, H2O AI, to implement the DL-based model. The proposed model was evaluated on a text recognition dataset sourced from the UCI repository. This exemplifies the power of DL for complex tasks such as text recognition, highlighting its relevance to the increasingly sophisticated demands of IIoT applications. Additionally, the authors of [130] introduced a DL-based framework using the MobileNet model for automatic machine and machine component detection. This model is integrated into their AR-based solution aimed at visualization, analysis, and interaction with machines in indoor environments. The effectiveness of this model underlines the potential of DL

to improve Industry 5.0 capabilities. In a different context, the authors of [131] utilized a GRU model to predict the high-dimensional thermal history in direct energy deposition (DED) processes. Their findings show that the use of GRU for thermal history prediction is not only highly accurate, but also adaptable across a broad spectrum of processing parameters and part geometries. Furthermore, in this study [132] an implementation of a novel CNN-based framework has been proposed to predict distortions in laser-based additive manufacturing (LBAM). This demonstrates the versatility of DL models, as they can be adapted to address complex and specific problems within the Industry 5.0 environment.

On the other hand, RL seems like a potential option in the context of Industry 5.0 when taking into account the dynamic nature of industrial operations. For example, multi-agent RL was utilized in the paper [133] for cooperative caching and workload offloading in Multi-access Edge Computing (MEC). The multi-agent deep deterministic policy gradients technique was specifically suggested by the authors for usage on actor-critic networks at each MTA (machine-type agent). The MTAs then swap their locally learned models to create a global model. The efficacy of this multi-agent MEC system is predicated on the idea that all edge nodes can interact flawlessly with one another throughout the training phase. However, due to inadequate connectivity between edge nodes and extremely dynamic network topologies in many real-world circumstances, such an assumption may not hold. Additionally, some frameworks combine RL with FL, as the one described in [134]. Within industrial IoT networks, the authors of this paper presented a deep-federated reinforcement learning (DFRL) technique to help with dynamic resource allocation and network management. To provide slice-based resource allocation in Industrial IoT, the plan used Deep Federated Q-Learning (DFQL). The authors presented two strategies: Multi-Agent Deep Q-Learning (MAQL), which dynamically slices transmit power and spreading factor to maximize Quality of Service (QoS) requirements, such as throughput and delay, and FL, which is used to learn a multi-agent model to facilitate decision-making on Industrial IoT virtual network slices.

Recently, an integrated FL-blockchain architecture was developed for industrial IoT networks [135], to offer security throughout the construction of IIoT systems. FL provides an
answer to Industry 5.0's privacy and productivity problems. Using sophisticated selection methods, blockchain technology (BCT) can be used to strengthen FL against the potential of poisoning attacks. This cutting-edge technology amalgam shows how safe and decentralized processes can be included in the Industry 5.0 framework. To add security to the implementation of FL, citing [136] also uses blockchain. The industrial sector has also seen extensive use of the GNN paradigm. For example, to simulate the spatio-temporal interdependence of heat reactions in additive manufacturing processes, the authors of [137] used a GNN model. Their findings highlighted the potential of GNN in the context of Industry 5.0 by showcasing enhanced performance on lengthy thermal histories of unknown geometries.

2) Robotics: GNN also plays an important role in the robotics field, especially in understanding and interpreting the environment as well as the interactions between different entities. A GNN works by considering entities as nodes and the interactions or relationships between these entities as edges, forming a graph [138]. For instance, the research conducted in [139] used robots to communicate, share information with neighboring entities, and select actions through a GNN-based learning framework. To capture the complex topological properties and node features of these graphs, a Variational Autoencoder (VAE) and GNN, known as NeatNet, have been proposed [140]. This innovative architecture extracts a low-dimensional latent preference vector from users based on their arrangement of scenes, offering a powerful approach to learning latent representations within graph-based systems.

In addition to GNN-based models, in the context of IIoT, RL also plays a significant role in the field of robotics, primarily because it offers a way for robots to autonomously learn optimal behavior from their own experiences. Unlike other forms of ML, which require a fixed training phase, RL allows robots to continuously learn and improve their performance over time. In this study [141], an end-to-end approach has been proposed for mobile robot navigation in unknown environments. By leveraging DRL, a mobile robot progressively acquires knowledge about its surroundings by exploring the environment. Consequently, it learns to navigate autonomously toward a target destination relying solely on an RGB-D camera for sensory input. This approach underscores the potential of DRL to facilitate self-guided navigation systems in the realm of robotics.

Similar to other IIoT applications, FL plays a crucial role in robotics in several aspects, such as collaborative learning, data privacy/security, communication overhead, and network efficiency. Numerous scholarly contributions have been made in this field. For instance, the authors of [142] and [143] explored a federated imitation learning approach for cloud robotics. Under this scheme, each robot contributes to the training of an imitation Neural Network (NN) using its unique sensor image dataset. The updated parameters are subsequently offloaded to the cloud for knowledge fusion, where the server aggregates the knowledge from various robots to construct a robust learning model. The aggregated knowledge is then disseminated back to the robots for the subsequent round of learning, enabling them to gain insights from the shared

knowledge. Using this FL approach, the efficiency and accuracy of local robots in imitation learning can be significantly enhanced compared to traditional centralized learning methods. This distributed learning strategy allows robots to draw on the knowledge and experiences of other robots, thereby augmenting their learning capabilities.

The importance of fog/edge computing in the context of vertical domains in 5G communications and beyond has been underlined in [144]. Fog/edge computing essentially decentralizes the cloud computing concept by relocating computational resources closer to the network edge, thereby facilitating lower-latency communication. In the realm of FL, fog/edge computing offers the computational, network, and storage resources required for robots to share, collaborate, and learn tasks collectively. Edge computing was also used with FL in [30] to facilitate cooperative learning between robotic devices. Due to the inherent complexity and dynamic nature of the system, each device operates its own unique RL model to formulate its control policy. After developing a mature policy model, the devices shared their parameters with a central cloud server for aggregation. The experimental results confirmed that the proposed method significantly enhances learning performance, especially in terms of speed, across different learning clients. Consequently, we can conclude that the combination of cloud computing and fog/edge computing can play a crucial role in enabling federated robotics, where robots can collaborate, exchange knowledge, and collectively improve their learning and performance through FL techniques.

## E. Lesson Learned

In this sub-section, we discuss the key lessons acquired from using AI in different IoT applications.

1) IIoT for Smart Healthcare: IIoT has the potential to revolutionize the realm of smart healthcare by infusing advanced AI functionalities. This transformation can improve healthcare services, improve predictive analyzes for patients, and reduce latency, all by fostering cooperation among various entities, including patients, healthcare providers, and medical institutions. For example, AI can provide different solutions to combat the COVID-19 pandemic in several ways, such as by supporting the prediction of outbreaks. We also find that shallow models can be easier to interpret and explain and may require fewer computational resources. However, their performance may not be that strong when dealing with complex patterns and large datasets. Additionally, CNN-based models have been a key component of image processing due to their ability to extract complex ideas from visual data. Before we can use the CNN-based model on a large scale, the computational cost must be addressed [46]. Modern CNN models in particular have great inference accuracy, but are becoming increasingly complicated with millions of parameters. The IoT is moving toward edge computing, which performs processing at or close to the data source, emphasizing the need for effective and lightweight models. This is due to the limited computational resources and storage capacity of edge devices. We also find that FL can provide flexible and
privacy-preserving healthcare applications. However, accord-
ing to [54], to address the issue of data heterogeneity in
distributed health IoT networks, personalized FL is essential
as it facilitates the development of high quality personalized
models.

2) IIoT for Smart City: AI techniques have been exten-
sively utilized to infuse intelligence into the realization of
smart cities. Their ability to manage large-scale data generated
from various sensors, devices, and human interactions in
real time gives them the ability to provide cities with smart
features. Most of the proposed AI-based smart city solutions
are based on DL models. These models are important for
extracting patterns from the data generated through ubiquitous
IoT devices. For example, DL helps improve the accuracy
and efficiency of surveillance systems, providing safer envi-
ronments by detecting anomalies or tracking objects across
camera networks. Also, DL can predict energy demand and
adjust supply accordingly, optimizing the use of renewable
energy sources and reducing energy waste. In addition, it
processes large volumes of environmental data from various
sensors in the city to predict water quality. Similarly, FL plays
a critical role in smart cities, particularly in addressing the
challenges related to data privacy and network efficiency. In
general, IIoT can enable smart cities to be more efficient,
intelligent, and privacy-preserving, making it a key technology
for their development and growth.

3) IIoT for Smart Transportation: Several possible appli-
cations of AI in smart transportation include autonomous
vehicles and traffic management. AI is the foundation for self-
driving cars and trucks, as it allows these vehicles to interpret
sensory data, make decisions in real time, and navigate the
environment safely without human intervention. It can power
advanced driver assistance systems (ADAS) to improve road
safety, offering features such as automatic braking, collision
avoidance, and lane departure warnings. Specifically, RL can
continually learn and adapt to new situations, which can
be particularly useful in complex and ever-changing traffic
scenarios. Moreover, a new generation of neural networks,
known as SNN, has been used for autonomous driving.
These models, known for their energy and computational
efficiency, can bring significant advances to the field of
autonomous vehicles, furthering the goal of full automation.
Furthermore, in the realm of traffic management, DL models,
especially GNNs, are revolutionizing the way we interpret
and respond to traffic data. These models can analyze real-
time traffic data, forecast congestion, and recommend the
most efficient routes for drivers. This is largely due to their
ability to extract significant information from complex, high-
dimensional, and unstructured datasets. Therefore, AI is not
only influencing, but actively shaping the future of smart
transportation.

4) IIoT for Smart Industry: Recently, IIoT has been intro-
duced to bring AI functions to the IoT to empower the
smart industry, including the advent of Industry 5.0 and
advances in robotics. Industry 5.0 often referred to as
the“cognitive industry”, marks a significant shift from automa-
tion to collaboration. It uses AI to enhance human-machine
interaction. The use of AI algorithms enables real-time

decision-making, predictive maintenance, and optimization of
production processes. In effect, AI contributes to more flexible,
efficient, and personalized production lines, while upholding
the value of human creativity and innovation. In particular,
RL can optimize process control and production planning,
leading to increased efficiency and reduced costs. RL can also
be used to train robots to perform complex tasks by learning
optimal strategies through trial and error. For example, an RL-
trained robot could learn the most efficient path to move parts
across a factory floor. However, GNNs offer valuable insight
and opportunities to improve various aspects of Industry 5.0,
thereby improving productivity and efficiency in the new
age of industrial operations. Furthermore, we find that RNN-
based models (e.g., GRU and LSTM) hold significant potential
for smart industry applications because industrial machinery
generally generates time-series data that can be analyzed to
predict failures.

In summary, ML, DL, RL, and FL solutions have great
potential in IIoT applications, their successful deployment
requires careful consideration of several factors, including data
quality, computational requirements, privacy, robustness, and
security.

IV. SECURITY ISSUES IN IIoT

As IIoT networks have become an integral part of our
lives, the use of learning algorithms in a variety of domains
(e.g., home, industry, and healthcare) can help commu-
nication processes and analytics with real-time responses.
However, the proliferation of IoT devices makes IoT mining
easy, and the lack of security standards and policies leaves
IIoT networks open to unauthorized attacks. Against this
background, understanding network attacks, confidentiality,
integrity, and intrusion is necessary to develop countermea-
sures.

A. Network Attacks

Network attacks, often known as cyberattacks, attempt to
obtain unauthorized access to or alter the regular operation of a
network or its components. These attacks can target any aspect
of a network. In what follows, we first present four typical
types of IIoT networks: denial-of-service attacks, poisoning
attacks, adversarial attacks, and membership inference attacks.
Then, we introduce some potential ways to evaluate the
security levels, along with some security standards.

1) **Denial-of-Service Attacks:** A denial-of-service (DoS) attack is a kind of malicious attack that exploits the availability of single or multiple interconnected IoT devices, sensors, or sources to disrupt IIoT networks' operation by flooding them with lots of malicious requests or traffic. This type of attack can target different layers of networks, such as the transport layer, network layer, or application layer. There are ways through which the attacker can be launched, such as the specific vulnerabilities involving DNS, SMURF, or ACK mechanisms, the limited resources of IoT devices (i.e., storage, bandwidth, authentication, authorization, encryption, and firmware), or algorithms to inject noise, poison data, or manipulate feedback to learning models.
<table><caption>TABLE IV
SUMMARY OF IIoT APPLICATIONS</caption><thead><tr><th>Application</th><th>Ref.</th><th>Use case</th><th>Model type</th><th>Contributions</th><th>Limitations and Challenges</th></tr></thead><tbody><tr><td rowspan="12">Smart Healthcare</td><td>[41]</td><td>Ambient Assisted Living</td><td>K-mediod, DBN, LSTM</td><td>A DL-model for remote sleep monitoring.</td><td>The computation cost has not been evaluated.</td></tr><tr><td>[42]</td><td>Ambient Assisted Living</td><td>PCA, SVM</td><td>A shallow ML-based model for remote body pressure monitoring.</td><td>The proposed scheme is simple and not scalable.</td></tr><tr><td>[43]</td><td>Ambient Assisted Living</td><td>CNN</td><td>A DL-based model to learn and extract human activity features without any domain knowledge.</td><td>The comparison with other state-of-art DL-based schemes has been missed.</td></tr><tr><td>[44]</td><td>Ambient Assisted Living</td><td>CNN</td><td>An investigation of DL model efficiency for features extraction for HAR.</td><td>The complexity of the proposed scheme has not been discussed.</td></tr><tr><td>[46]</td><td>Ambient Assisted Living</td><td>CNN</td><td>A computation-efficient DL-based model for AAL.</td><td>Privacy of data has not been taken into account.</td></tr><tr><td>[47]</td><td>Ambient Assisted Living</td><td>CNN, LSTM</td><td>A spatio-temporal features extraction in AAL.</td><td>The model was trained in a fully supervised way.</td></tr><tr><td>[50]</td><td>Ambient Assisted Living</td><td>LSTM</td><td>An FL-based approach for the HAR predictor in a smart environment.</td><td>The scalability of the FL scheme has not been discussed.</td></tr><tr><td>[52]</td><td>Ambient Assisted Living</td><td>MLP, AE</td><td>A semi-supervised FL scheme for HAR.</td><td>The communication cost has not been evaluated.</td></tr><tr><td>[53]</td><td>Ambient Assisted Living</td><td>DNN</td><td>A semi-supervised federated Active learning framework for HAR.</td><td>The impact of the non-IID data has not been investigated.</td></tr><tr><td>[54]</td><td>Ambient Assisted Living</td><td>CNN</td><td>Personalised FL model to mitigate the negative effects caused by statistical heterogeneity.</td><td>The system heterogeneity has not been taken into account.</td></tr><tr><td>[55]</td><td>Ambient Assisted Living</td><td>-</td><td>A resource-Aware Lightweight FL model for AAL.</td><td>The energy consumption of the proposed scheme has not been discussed.</td></tr><tr><td>[56]</td><td>Ambient Assisted Living</td><td>Ensemble model</td><td>An heterogeneous federated stacking model to monitor patient activities.</td><td>Privacy-related issues in the proposed scheme have not been investigated.</td></tr><tr><td>[59]</td><td>COVID-19</td><td>SVM</td><td>An early detection of COVID-19 based on shallow model.</td><td>The scalability issue of SVM has been ignored.</td></tr><tr><td>[61]</td><td>COVID-19</td><td>CNN</td><td>A CNN-based models for COVID-19 images classification.</td><td>A small dataset has been used for the performance evaluation.</td></tr><tr><td>[63]</td><td>COVID-19</td><td>CNN</td><td>A Transfer Learning scheme for COVID-19 image classification.</td><td>The model was trained in a fully supervised way.</td></tr><tr><td>[66]</td><td>COVID-19</td><td>-</td><td>A 5G-enabled architecture for the diagnosis of COVID-19 based on FL.</td><td>One dataset has been used for performance evaluation.</td></tr><tr><td>[67]</td><td>COVID-19</td><td>CNN</td><td>A dynamic fusion-based FL method to improve communication efficiency and model performance to detect COVID-19-infected patients.</td><td>The effect of the non-IID data has not been investigated.</td></tr><tr><td>[68]</td><td>COVID-19</td><td>LSTM</td><td>An FL framework to predict the dynamics of the COVID-19 outbreak.</td><td>The scalability of the model has not been discussed.</td></tr><tr><td rowspan="12">Smart City</td><td>[73]</td><td>Smart Grids</td><td>-</td><td>An ML-based energy management framework to mitigate demand-side energy management issues.</td><td>A comparative analysis of different MLs has been ignored.</td></tr><tr><td>[74]</td><td>Smart Grids</td><td>LSTM</td><td>A Multidirectional LSTM model to predict the stability of a smart grid network.</td><td>Convergence latency should be included.</td></tr><tr><td>[75]</td><td>Smart Grids</td><td>RNN</td><td>A delay-deadline constrained FL framework to predict link reliability in mobile edge computing of a grid.</td><td>The impact of the straggler nodes on local training has not been considered.</td></tr><tr><td>[76]</td><td>Smart Grids</td><td>NN</td><td>An FL framework for power learning in IoT networks comprising electric providers and users.</td><td>Comparison of ML models in FL simulation has been missed.</td></tr><tr><td>[77]</td><td>Smart Grids</td><td>DRL</td><td>An FL approach for secure and efficient personal energy data sharing using edge-cloud collaboration.</td><td>Model security has not been investigated.</td></tr><tr><td>[78]</td><td>Smart Grids</td><td>RNN</td><td>A cloud-edge FL-based scheme for the prediction of energy demand.</td><td>One dataset has been used for the performance evaluation.</td></tr><tr><td>[81]</td><td>Underwater</td><td>YoLo 4</td><td>A novel technique to monitor water consumption using an optical character recognition device.</td><td>No details simulations are given.</td></tr><tr><td>[82]</td><td>Underwater</td><td>CNN</td><td>An integrated automatic detection model to identify temporal resolution and high spatial imagery systems.</td><td>Convergence of the model has not been verified.</td></tr><tr><td>[84]</td><td>Underwater</td><td>BiLSTM</td><td>A DL-based model for water quality prediction.</td><td>The computation cost of the proposed scheme has not been discussed.</td></tr><tr><td>[85]</td><td>Underwater</td><td>Auto DL</td><td>Auto DL model for water quality prediction.</td><td>Data privacy has not been investigated.</td></tr><tr><td>[87]</td><td>Underwater</td><td>NN</td><td>A federated Meta-Learning Enhanced Acoustic Radio Cooperative Framework.</td><td>The effect of the non-IID data has not been evaluated.</td></tr></tbody></table>

There are different ways to classify DoS attacks in IIoT networks, depending on the criteria or perspective used. For the target layer of the network [145], DoS attacks can be distinguished by three types: (1) connection-oriented services of the transport layer: SYN flooding, RST flooding, ACK flooding and FIN flooding; (2) the routing or forwarding functions of the network layer: ICMP flooding, SMURF, FRAGGLE, and LAND; and (3) application-specific services or protocols of the application layer: HTTP flooding, DNS amplification, MQTT flooding, and NTP amplification. From the perspective of source or technique [146], DoS attacks have three types. First, a single-source DoS attack adopts UDP flooding, ICMP flooding, and HTTP flooding methods to send a large amount of traffic or requests to a target service or

network. Second, a multi-source DoS attack uses TCP-SYN flooding, DNS amplification, or SMURF to form a botnet or a network of zombies that coordinate the attack. Third, a reflective DoS attack relies on a third-party service or network as a reflector to amplify the attack traffic or requests. A typical attack example is that an attacker sends spoofed packets or requests based on the amplification mechanisms of DNS, NTP, and SSDP to the reflector, which then responds to the target with a larger amount of traffic or requests than the original ones. In addition to that, DoS attacks can also be classified based on impacts in that this affects ML-based systems [147], consisting of:

* Poisoning data: A volume of malicious or noisy data will be injected into learning-based systems to corrupt the
<table><caption>TABLE V
SUMMARY OF IIoT APPLICATIONS (CONTINUED)</caption><thead><tr><th>Application</th><th>Ref.</th><th>Use case</th><th>Model type</th><th>Contributions</th><th>Limitations and Challenges</th></tr></thead><tbody><tr><td rowspan="14">Smart Transportation</td><td>[91]</td><td>Autonomous Vehicles</td><td>CNN</td><td>An automatic end-to-end learning for large-scale detection and recognition of traffic signs through.</td><td>The computation cost has not been discussed.</td></tr><tr><td>[92]</td><td>Autonomous Vehicles</td><td>CNN</td><td>An attention network to extract more relevant features from the input data.</td><td>The convergence of the proposed scheme has not been verified.</td></tr><tr><td>[93]</td><td>Autonomous Vehicles</td><td>Transformers</td><td>A Multiple spatial transformers for traffic.</td><td>The generalization capability of the proposed scheme has not been evaluated.</td></tr><tr><td>[94]</td><td>Autonomous Vehicles</td><td>CNN</td><td>A knowledge distillation-based model to reduce the computation cost.</td><td>A small dataset has been used for the performance evaluation.</td></tr><tr><td>[95]</td><td>Autonomous Vehicles</td><td>SNN</td><td>An energy FL-based scheme for accurate recognition models.</td><td>The communication cost has not been verified.</td></tr><tr><td>[97]</td><td>Autonomous Vehicles</td><td>KNN</td><td>A mobile application that can estimate energy availability and/or expected charging finishing time.</td><td>The scalability of the proposed scheme has not been explored.</td></tr><tr><td>[98]</td><td>Autonomous Vehicles</td><td>NN</td><td>A Federated Energy Demand Learning Scheme for Energy Demand Prediction.</td><td>Training latency has not been analyzed.</td></tr><tr><td>[99]</td><td>Autonomous Vehicles</td><td>RL</td><td>A Markov decision process to choose the amount of energy to be charged.</td><td>The choice of the model hyperparameters has not been explored.</td></tr><tr><td>[100]</td><td>Autonomous Vehicles</td><td>LSTM</td><td>A novel approach to model the behavior of plug-in electric vehicles in the energy market.</td><td>The privacy of the collected data has not been taken into account.</td></tr><tr><td>[101]</td><td>Autonomous Vehicles</td><td>LSTM</td><td>An FL-based placement strategy for idle mobile charging stations.</td><td>The communication cost of the proposed scheme has not been evaluated.</td></tr><tr><td>[106]</td><td>Traffic management</td><td>KNN</td><td>An improved KNN model to improve forecast accuracy based on spatiotemporal correlation and achieve multistep forecasting.</td><td>The generalization capability of the proposed scheme has not been verified.</td></tr><tr><td>[107]</td><td>Traffic management</td><td>RF</td><td>A shallow sample model for traffic prediction.</td><td>The proposed model is simple.</td></tr><tr><td>[116]</td><td>Traffic management</td><td>GCN, LSTM</td><td>An hybrid GNN and LSTM to better capture the temporal dependencies of traffic flow.</td><td>One dataset has been used for the performance evaluation.</td></tr><tr><td>[118]</td><td>Traffic management</td><td>GRU</td><td>A clustering-based FL scheme to efficiently capture spatio-temporal correlation without privacy concerns.</td><td>Privacy and security performance have not been evaluated.</td></tr><tr><td>[121]</td><td>Traffic management</td><td>RL</td><td>An intelligent RSU scheduling algorithm based on Q-learning.</td><td>The proposed scheme is not scalable to multi-agent systems.</td></tr><tr><td>[122]</td><td>Traffic management</td><td>Multi-agent RL</td><td>Enhancing the utility and cover a more extensive area.</td><td>Communication privacy has not been taken into account.</td></tr><tr><td rowspan="14">Smart Industry</td><td>[125]</td><td>Industry Intelligence</td><td>SVM, DT, RF, KNN</td><td>A shallow model to automate material identification.</td><td>The scalability of the proposed schemes has not been evaluated.</td></tr><tr><td>[126]</td><td>Industry Intelligence</td><td>RF</td><td>An automatic monitoring and analysis of the performance of heating, ventilation, and air conditioning systems.</td><td>Numerical results for performance evaluation are lacking.</td></tr><tr><td>[127]</td><td>Industry Intelligence</td><td>RF, XGBoost</td><td>A DT-based models to predict machine failure.</td><td>The proposed work is simple with a lack of detail analysis.</td></tr><tr><td>[130]</td><td>Industry Intelligence</td><td>CNN</td><td>A DL-based framework for automatic machine and machine component detection.</td><td>The computation cost of the proposed scheme has not been evaluated.</td></tr><tr><td>[131]</td><td>Industry Intelligence</td><td>GRU</td><td>A DL scheme to predict the high-dimensional thermal history in directed energy deposition processes.</td><td>The comparison with other state-of-the-art DL-based schemes has been missed.</td></tr><tr><td>[132]</td><td>Industry Intelligence</td><td>CNN</td><td>A novel DL-based framework to predict distortions in laser-based additive manufacturing.</td><td>The generalization capability of the proposed scheme has not been verified.</td></tr><tr><td>[133]</td><td>Industry Intelligence</td><td>Multi-agent RL</td><td>A multi-agent RL scheme to reduce the computation delay and increase the channel access success rate in industry IoT without knowing the system parameters in advance.</td><td>The security risk of the IIoT devices has been ignored.</td></tr><tr><td>[134]</td><td>Industry Intelligence</td><td>RL</td><td>A federated RL to facilitate dynamic resource allocation and network management within industrial IoT networks.</td><td>The communication cost of the proposed scheme has not been evaluated.</td></tr><tr><td>[136]</td><td>Industry Intelligence</td><td>GNN</td><td>A FL-based blockchain scheme to provide security for FL implementation in industry IoT networks.</td><td>Training latency has not been analyzed.</td></tr><tr><td>[137]</td><td>Industry Intelligence</td><td>GNN</td><td>A GNN model to capture the spatiotemporal dependencies of thermal responses in additive manufacturing processes.</td><td>The computation cost of the proposed schemes has not been verified.</td></tr><tr><td>[139]</td><td>Robotics</td><td>GNN</td><td>GNN models to capture local interactions of robots and learn decentralized decision-making for robots.</td><td>The communication cost has not been explored.</td></tr><tr><td>[140]</td><td>Robotics</td><td>VAE, GNN</td><td>VAE architecture using GNN to extract a low-dimensional latent preference vector from a user.</td><td>The models' complexity has not been discussed.</td></tr><tr><td>[141]</td><td>Robotics</td><td>DRL</td><td>An end-to-end DRL-based approach for mobile robot navigation in unknown environments.</td><td>The proposed model has not been evaluated with dynamic environments.</td></tr><tr><td>[142]</td><td>Robotics</td><td>NN</td><td>A federated imitation learning approach for cloud robotics with heterogeneous sensor data.</td><td>The scalability of the proposed scheme has not been investigated.</td></tr><tr><td>[143]</td><td>Robotics</td><td>RL</td><td>An FL-based scheme for robotic management</td><td>Data loss caused by communication has not been considered.</td></tr></tbody></table>

training or the feedback data process, thereby degrading the performance or accuracy of the learning model or algorithm or even causing it to produce wrong or harmful outputs. For example, an attacker can poison the data of learning-based systems that control a smart traffic light by sending fake vehicle counts or speeds, causing traffic jams or accidents.

* Model evasion: These attacks craft adversarial input to evade detection or classification of learning-based systems, allowing them to bypass security or functionality. For example, an attacker alters the power consumption patterns or adds noise to avoid detection of the smart grid, causing power outages or damage.
*   **Model extraction:** These attacks query learning-based systems with carefully designed inputs and observe their outputs to extract or steal the parameters or the structure, leading to critical issues of intellectual property and privacy, or even enabling further attacks such as model evasion or poisoning. For example, an attacker sends fake patient records or symptoms to extract the model of a smart health service, causing financial losses or privacy breaches.

DoS attacks in intelligent IoT networks might jeopardize the security, privacy, and operation of the devices, services, and applications [148], and some of the potential risks that this type of attack brings, therefore, include:

*   **Service disruption:** The availability and reliability of IoT services or smart applications (e.g., health, network, and transportation) can be disrupted or deteriorated, causing users or consumers annoyance, irritation, or even danger. For example, a DoS attack on a smart metering network can disrupt the billing and payment process, or in a connected vehicle network it can interfere with navigation and safety features.

*   **Resource exhaustion:** IoT devices or networks with limited resources in terms of battery, memory, bandwidth, and CPU can be consumed or drained, resulting in decreased performance or functionality, or even irreversible damage. For example, a DoS attack on a smart farming network can drain the batteries of the sensors and actuators, or in an industrial automation network it can overload the controllers and actuators.

*   **Data loss of theft:** Uncertainty about the integrity or confidentiality of data collected, processed, or transmitted by IoT devices or networks can result in data corruption, deletion, leakage, or manipulation, which can violate the privacy or security of users or organizations who own or use the data.

*   **Financial losses:** The consequences involved of DoS attacks on individuals or organizations could be distinguished by: (1) direct losses, mostly the costs of repairing, replacing, and upgrading IoT devices/networks, paying fines/compensations, and losing revenues/customers; and (2) indirect losses, normally the costs of losing reputation, trust, or competitiveness in the market.

To reduce the potential risks brought about by DoS attacks, the implementation of IIoT networks should consider some of the following possible solutions. First, building out features involved network monitoring and anomaly detection by adopting NetFlow and SNMP approaches and/or learning-based solutions, for example, IDS or intrusion prevention system (IPS), to identify anomalous or malicious traffic patterns (e.g., volume, frequency, source, destination, and protocol), thus being able to detect DoS attacks in time and trigger appropriate countermeasures, such as filtering, blocking, or diverting the attack traffic. Second, it is considered to employ a software-defined networking approach in order to separate the control and data planes and centralize network management and configuration, increasing the visibility and flexibility of IIoT networks, as well as enabling dynamic changes and adaptive

responses to DoS attacks, for example, routing paths, bandwidth allocation, and security policies. Third, the adoption of consensus mechanisms in BCT in distributed and decentralized networks helps them verify and validate any transactions or events from the network entities, which enhances the security and trustworthiness of the network and prevents spoofing or tampering with network data or devices.

2) **Poisoning Attacks:** Poisoning attacks are a type of attack that takes advantage of vulnerabilities in learning-based systems (e.g., insecure data collection and transmission, lack of encryption and authentication, heterogeneous and distributed architecture, and limited resources and capabilities) to corrupt or degrade their ML models through introducing malicious data into the dataset. This type of attack is often executed before or during the model's learning or training phase. Usually, it takes advantage of some techniques such as label flipping, data injection, data modification, or data deletion [149].

Based on the attacker's goal and method, poisoning attacks can be distinguished by twofold. First, called poisoning attacks, the attacker aims to destroy the ML model, causing it to learn wrong or biased patterns, such as overfitting, underfitting, or skewed distribution. Automated transportation, healthcare, and industrial control systems are potential targets, where the two most frequent targets are recommender systems [150] and crowdsourcing systems [151]. Second, called backdoor poisoning attacks, the attacker aims to implant a hidden functionality or behavior into the ML model, making it produce incorrect or undesirable outputs for specific inputs that trigger the backdoor, such as a predefined class or label. An illustration of this attack is that adversaries, through the aid of internal staff or intrusion systems, refine the centralized and formatted iterative parameters in centralized learning networks or pretend to be legitimate clients to upload infected parameters to IIoT servers in distributed learning networks.

The involvement of multiple IoT devices/targets and the heterogeneity of system architectures coexisting in IIoT networks make them vulnerable to poisoning attacks. Consequently, there are several potential risks that poisoning attacks pose to IIoT networks, such as

*   **Data leakage or manipulation:** Data collected and processed by IoT devices (e.g., sensors, cameras, or smart meters) may have their confidentiality and integrity compromised. In smart agriculture, industrial automation, or smart metering applications, the leakage of sensitive information from these systems leads to privacy breaches, financial losses, or operational failures. For instance, if smart farming networks train their learning model based on poisoned data, the farmers' personal information trained by these models can be disclosed, which might then be used to manipulate the crop yield predictions or sabotage the irrigation system.

*   **Model misbehavior or malfunction:** It compromises the functionality and reliability of the learning model deployed on edge devices or IoT devices, such as network intrusion detection systems (NIDS), traffic classification systems, or recommendation systems. For example, when the labels or features of the training data in a medical
diagnosis system are modified, the learning model may misdiagnose patients or prescribe the wrong treatments, endangering their lives. On another front, the learning model might miss real attacks, raise false alarms, or favor certain attackers when network intrusion detection in IoT environments is trained with poisoned data.

*   **Physical harm or damage:** The operation of the IIoT system is not safe and secure. As training data are poisoned, the predicted outcome or guidance of the learning model can be erratic or dangerous in certain situations, such as ignoring traffic signs, colliding with obstacles, or attacking humans, leading to accidents, injuries, or deaths. For example, autonomous driving in IoT environments can ignore stop signs, swerve into other lanes, or hit pedestrians.

*   **Distributed services:** The availability and scalability of the IoT network, such as cloud servers, edge servers, or IoT devices, become invalid. For example, adversarial examples generated using GANs might be exploited to poison the FL process among multiple IoT devices or edge servers, making them overload their CPU and memory usage, thereby slowing down their response time, causing network congestion, or even shutting down the network services.

From the above risk, it is clear that to protect and maintain the operation of IIoT networks, there are three critical works. First, training data should be cleaned of any noise, outliers, or anomalies before feeding them to the learning model, reducing all possible impacts of poisoning attacks. Second, the machine learning model is required to be able to resist poisoning attacks using different methods, such as regularization, outlier detection, or adversary training. Third, data aggregation must be protected from multiple sources in a distributed machine learning system using cryptographic protocols, such as homomorphic encryption (HE) or secret sharing.

3) **Adversarial Attacks:** Adversarial attacks are a type of attack that focuses on generating slightly modified inputs from the original ones to deceive an ML model into producing incorrect or undesirable outputs, such as misclassification, misrecognition, or misbehavior. Typically, such attacks exploit the vulnerability of IIoT networks that involve biased training data, vulnerable underlying models, and fabricated features, and these attacks mainly target the inference or testing phase in which the learning model has been trained and deployed.

Adversarial attacks can be classified in different ways, depending on the criteria used or the knowledge models. From a criterion perspective, there are four main types of adversarial attacks. First, non-targeted attacks cause learning models to generate incorrect or undesirable output without specific class or label control using small perturbations or random noise. Second, targeted attacks attempt to make learning models produce incorrect or desirable outputs using methods such as optimizing perturbations or creating adversarial examples that resemble the target class. Third, universal attacks cheat learning models to produce incorrect or undesirable outputs for input data using methods like finding perturbations or generating adversarial examples. Fourth, physical attacks aim to manipulate learning models to produce incorrect outputs for

real-world input data using methods like modifying objects, adding stickers, or projecting images or patterns. In comparison, non-targeted attacks are the most general and easiest type of attack, as they do not require any knowledge or control over the output as in targeted attacks. In contrast, universal attacks do not require any knowledge or access to the input data, and thus they are more challenging and powerful than targeted or non-targeted attacks. However, physical attacks are said to be more practical than the three above attacks, as they do not require any digital manipulation of the input data. Meanwhile, the classification of adversarial attacks based on the knowledge models includes:

*   **White-box attacks:** The attacker has full or partial access and information about the learning model (i.e., architecture, parameters, gradients, or training data), which are then exploited in conjunction with techniques, such as gradient-based methods, optimization-based methods, or transformation-based methods, to craft more effective and targeted adversarial input [152]. For example, an attacker may use white-box attacks to fool an image classifier that uses a CNN model by using a fast gradient sign method or other gradient-based methods.

*   **Grey-box attacks:** The attacker has some access and information on learning models, including the kind of model, number of layers, or activation functions, but not enough exact weights or values to perform a white-box attack. Gray-box attacks typically use this partial knowledge in combination with zeroth-order optimization methods, decision-based methods, or score-based methods to improve the efficiency and effectiveness of black-box attacks [153]. For example, an attacker may use gray box attacks to compromise a face recognition system that builds a deep-metric learning model by using model extraction or other optimization techniques.

*   **Black-box attacks:** The attacker has no or limited access to information about the learning model (e.g., input-output behavior, confidence scores, or feedback). This type of attack only relies on trial-and-error methods or surrogate models to generate adversarial input using query-based methods, transfer-based methods, or generative methods [154]. For example, an attacker may employ black-box attacks to bypass a network intrusion detection system that uses a DL model in an IoT environment.

As adversarial attacks have diverse attack methods, the potential risk produced by this attack also leads to numerous disadvantages to the development of IIoT networks, including

*   **Data leakage or manipulation:** Data processed by intelligent networks, such as patient records, financial transactions, or personal information, can be compromised in terms of confidentiality and integrity. For example, an attacker may use structured query language injection to access or modify sensitive data in a Web application's database that uses IoT devices to collect and store data.

*   **Device impersonation or hijacking:** The authenticity and availability of devices connected to the intelligent network, such as medical devices, smart meters, or cameras, are no longer guaranteed. For example, an attacker
may use hard-coded keys to decrypt communications or spoof the identities of legitimate devices on an IoT medical device interface.

*   **Network intrusion or disruption:** The functionality and reliability of the intelligent network, such as NIDS, traffic classification systems, or device identification systems, can be destroyed. For example, an attacker may use adversarial examples to evade detection or mislead classification by a DL-based NIDS in IoT environments.

*   **Physical harm or damage:** The safety and security of the physical world, such as autonomous vehicles, drones, or robots, have vanished. These allow an attacker, for example, to use stickers or paint to create an adversarial stop sign that an autonomous vehicle will interpret as a yield or other sign.

To address these risks, learning-based systems are required to add some features. First, adversarial training; this approach allows the target systems to be trained from both normal and adverbial examples, and hence it can learn to resist gradient-based and black-box attacks. Second, the defensive distillation, the learning model is trained as a distilled model capable of mimicking the original model but with less sensitivity to input variations, making it harder for the attacker to generate adversarial examples. Third, randomness is added to the input or output of the target system, making it unpredictable for the black-box or gray-box attacker.

4) **Membership Inference Attacks:** This type of attack refers to the case where the adversary attempts to infer whether the data of a given IoT user are trained by a training/query learning model [155]. The information conjectured by the adversary is then applicable to launch the physical-layer authentication of heterogeneous IoT devices or service providers [156]. Typically, an attack workflow includes three phases: (1) creating a shadow model dataset that emulates the original training dataset based on the knowledge of black-box mode, white-box mode, and gray-box mode; (2) training shadow models to mimic the target model; and (3) developing attack classifiers to acquire differences between the target model on training data and behavior on unseen inputs. Membership inference attacks can be classified into three levels:

*   **Model-based attacks:** The adversary uses the outputs generated by the learning model, such as predicted class labels or probabilities, to deduce if the behavior of the model is in agreement with the individual's data during training [157].

*   **Data-training-based attacks:** The adversary uses the model's training logs or side-channel information during the training to extract information related to the model's training data (i.e., size, structure, or distribution), from which the necessary information is inferred [158].

*   **Model-free attacks:** This form of attack does not require knowledge of the learning model or training data. Instead, it relies on statistical tools to analyze and identify discrepancies between the model output and a set of known outputs [159].

Accordingly, the potential risks of this attack on IIoT networks can be faced as follows:

*   **Privacy violations:** By successfully inferring whether a particular user's data were part of the training set, an attacker can compromise that user's privacy, which consists of the user's behavior, habits, or other personal information.

*   **Information leakage:** Sensitive data, such as health records or financial information, could be exposed by this attack if access is successfully launched.

*   **Undermining integrity:** This attack can manipulate the model's output by feeding it with adversarial data, leading to incorrect or harmful decisions being made based on the manipulated data.

*   **Loss of trust:** If users lose confidence in IoT networks' security and privacy due to successful membership-inference attacks, they may be less likely to use or even have no adoption of smart applications in the future.

From the above risks brought by membership inference attacks, IIoT systems should consider the three techniques listed below. First, differential privacy exploitation can prevent an attacker from deducing the membership status of a data sample by adding random noise to the data or model parameters. Second, applying adversarial regularization, which trains the model to minimize the difference between the outputs of member and non-member samples, prevents an attacker from distinguishing them. Third, taking advantage of proactive defense, i.e., building a shadow model and fooling the attacker by sending false alerts or misleading information, is to reduce membership inference attack accuracy and prevent information leakage from the wireless signal classifier.

5) **Attack Measurements:** From the above-mentioned examples, it can be seen that there are various types of network attacks. Therefore, it raises the question of how to evaluate the security level of existing IoT networks. In fact, there are many ways, and they depend on each application-specific. 1) Perform a threat and risk analysis to identify assets, threats, vulnerabilities, and impacts of IoT security breaches [160], 2) Apply a security requirements engineering approach to evaluate and prioritize security requirements for IoT devices, software, and data [161], 3) Use a security testing framework to assess the security properties and performance of IoT devices and software under different scenarios and attacks [162], [163], 4) Using a security evaluation standard or certification scheme to measure the security level of IoT devices and software against predefined criteria and best practices [164]. Moreover, the method should evaluate the security of AI models and IIoT systems before they are deployed in practice. Therefore, the security evaluation framework should be developed and designed considering the large-scale nature of IoT systems, non-IID data characteristics of AI training data, and the heterogeneous nature of network scenarios. Furthermore, it is important to develop security methods towards the standardization of IoT, AI, communications, and IIoT. There have been several standardization bodies, such as the Security Evaluation Standard for IoT Platforms (SESIP) [165], Common Criteria for Information Technology Security Evaluation (CC) [166], IEEE 802.11 series [167], ETSI TS 103 645 [168], and details of other standards with specific use cases [169], [170]. It
is expected that there will be more and more collaborative research work and projects between different stakeholders in this exciting IIoT area, and this may result in the development of new standards or significant updates to the existing standards of security and privacy in IIoT.

## B. Confidentiality

Confidentiality is one of the security criteria for IIoT networks, as data and information created by IoT devices and systems should be available only to authorized users and systems. Confidentiality can help prevent unlawful exposure or leakage of sensitive or private information, such as personal information, health records, and location data. To ensure confidentiality in IIoT networks, there are three main approaches: cryptography, access control, and information flow tracking.

1) **Cryptography:** Cryptography encrypts the data using mathematical techniques to make them unreadable to unauthorized parties. For example, a user may need to have a secret key or a certificate to decrypt the data. Despite cryptography, which can protect data during transmission or storage, it cannot ensure data secrecy once it has been decrypted. Furthermore, the scarcity of resources from IoT devices limits their ability to perform complex cryptographic operations or store large quantities of keys or certificates. In this context, it is necessary to develop confidentiality solutions for IoT that are lightweight, efficient, and adaptive to resource constraints.

In terms of the above requirements, a good solution for encryption of raw IoT data during data acquisition is to use the compressive sensing scheme to increase secure interactions between IoT devices and the cloud [171]. Furthermore, the application of symmetric and asymmetric cryptosystems, that is, the advanced encryption standard (AES), the message digest algorithm (MD5), and the elliptic curve cryptography (ECC), should also be combined transmission protocols to encrypt the data during transmission [172].

In particular, exploiting outsourced services, such as fog and edge computing, to reduce the computational and storage burden on IoT devices might be prone to vulnerability with sensitive information. In this context, HE emerges as a powerful technique for computing encrypted data without decrypting it. However, ensuring real-time response with HE in IoT networks is a challenge. Driven by this fact, to speed up encryption and decryption operations and reduce the power consumption of HE, some specialized hardware has been realized, such as the Cheetah acceleration architecture for server-side HE DNN inference in [173], a new hardware architecture for Brakerski, Vaikuntanathan full HE scheme using Field Programmable Gate Array (FPGA) [174], and a novel confused modulo projection-based fully HE algorithm using graphics processing units (GPUs) to support floating-point operations [175]. On the other hand, exploiting HE is not an easy task for IoT devices due to its complex nature. To this end, the authors of [176] proposed two HE techniques. The first technique is to encrypt plaintext with somewhat HE while converting somewhat HE ciphertext to fully HE ciphertext on the cloud service side. The second technique is

to encrypt plaintext with TRIVIUM and the TRIVIUM key is then encrypted with somewhat HE. In [177], the authors proposed a suite of context-sensitive encryption protocols that only encrypt data according to the device specifications and the level of confidentiality of the data, while the rest are in plaintext. In [178], the authors introduced lightweight privacy-preserving data aggregation for fog computing-enhanced IoT based on the combination of homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques. Another illustrative scheme is to employ a new energy-efficient IoT security system in [179]. With proportional offloading, secure MQTT protocols enable IoT devices to offload data to fog nodes and/or cloud centers according to the computational power required by message packets to effectively use energy. At fog nodes and/or the cloud, the MQTT payloads will then be encrypted using a HE technique, called ECC-ElGamal.

Meanwhile, data aggregation and management are becoming difficult due to the diverse types of IIoT architectures. To deal with such demands, some recent work has built several learning-based networks that combine encryption approaches during training and learning. For instance, a DL-based network has been proposed in [180] to encrypt and decrypt the medical image. The work in [181] presented an ML-based AES algorithm against adversaries. To prevent risks involving model extraction and reverse attacks, the work in [182] proposed a data aggregation framework for distributed FL IoT scenarios. As described at the bottom of Fig. 7, the user data will be locally trained and encrypted before aggregating by the server using the masked trained model to generate a global model. Unlike [182], the work in [183] designed a blockchain-enabled HE model to achieve the double goal of data privacy and model privacy. As shown in the top of Fig. 7, after locally training raw data from IoT environments, the generated local results are uploaded to the cloud. In that regard, the global aggregation and update model procedure includes two steps. First, the RAFT consensus algorithm is utilized to achieve logical separation for distributed computing, where the data executor allocates some distributed data streams on multiple nodes indexed from 1 to N with the same set of state transitions. In every node, the node manager regulates the data analysis and database as SparkContext for the Spark function, while the resource manager administers all nodes via the Spark computing platform. Second, data protection aggregation tactics are adopted, where data from multiple nodes are encrypted using differential privacy and HE based on the respective Laplace and Paillier cryptosystems. These encrypted data will then be separately trained by K-means, random forest, and AdaBoost. As such, all data are summarized, while private models can be aggregated. Later, these global outcomes are fed into the federation procedure to construct a federation model.

2) **Access Control:** Access control governs established norms and rules to ensure that only authorized entities have access to data. For example, a user may need to provide a password, a biometric feature, or a role to access the data. However, access control cannot manage how the data are
<figure><img src="image_8.png" /><figcaption>Fig. 7. Data FL aggregation using blockchain-enabled EH (the top) and local encryption (the bottom).</figcaption></figure>

used or propagated after they are accessed. Furthermore, the exchange of large volumes of data among a large number of heterogeneous IoT devices and systems is quite difficult to build and deploy scalable confidentiality solutions that can handle the diversity and complexity of IoT situations.

Following that, access control protocols must design efficient key management mechanisms capable of flexibly generating, distributing, updating, and revoking keys (e.g., a unique password, zero-knowledge proof, mutual authentication, public-key cryptography, and digital signatures) for IoT devices in a secure and scalable manner. Depending on each application, the realization of key management mechanisms can be built by the following criteria:

a) **Authentication factor:** IoT environments constrained by physical size, internal capacity, and other storage allocations define two authentication factors. First, identity factors refer to the use of cryptographic techniques, such as symmetric, asymmetric, and hash functions [184]. Second, context factors are based on the basic characteristics of wireless communication to generate keys, tags, and fingerprints [185].

b) **Token-based authentication:** The communication session occurs for a short period of time in digital IoT contexts, which are particularly sensitive to temporal fluctuations and high-security standards. Instead of using a combination of username or password per communication, token-based authentication offers a strong link between users and smart devices, while reducing the risk of stolen

authentication factors for misuse, such as autonomous vehicle systems [186] or biometric systems [187].

c) **Authentication process:** For IoT systems built with a trusted entity, one-way authentication can be applied to achieve lightweight communication, for example, using human body characteristics (e.g., voice, face, fingerprint, and gait) in biometric traits authentication [188]. However, such a scheme is susceptible to spoofing or impersonation in complex IoT environments (e.g., smart health, smart home, smart cities). To protect IoT information between any two IoT entities and / or with third-party service providers, it is required to use two-way authentication [189] or three-way authentication [190].

d) **Authentication methodology:** To efficiently manage the identity of the accessing devices, IIoT networks tend to stipulate their architecture in terms of centralization, distribution, or decentralization. Thanks to a trusted third party (TTP), centralized authentication allows communication entity credentials to be managed and shared to ease the burden of infrastructure deployment. Meanwhile, to reduce peer-to-peer authentication failures through IoT nodes, servers, or base station in centralized fashions, distributed and decentralized authentications are administered at both nodes-devices and the network [191].

e) **Authentication tier:** The authentication mechanism is distinctly specified by perception tiers, communication
tiers (including data link, network, and transport layers), and application tiers [192]. The perception level interacts directly with the environment to collect raw IoT data, which requires a proper authentication mechanism for all hardware involved in the IoT network to combat tag cloning, RFID eavesdropping, fake information broadcasting, and RF jamming approaches before launching a DoS attack. At the communication level, the information collected by the perception layer will be synchronized and transferred over the mobile/private network, the wireless and wired network, and the communication protocols; however, the authentication mechanism could easily be destroyed due to network congestion with large amounts of data transmitted by DDoS attacks. Meanwhile, the application-tier interface mostly delivers custom-made services based on the individual user's requirements.

### 3) Tracking Information Flow:
This method tracks how data flows within the system and prevents it from reaching places where the confidentiality policy is violated. For example, IoT users are required to have a certain security level or permission to access the data. Information flow analysis can enforce the confidentiality policy throughout the system; however, it is difficult to implement or verify in complex or dynamic scenarios when sharing and storing data.

In this context, blockchain-based technology has been recognized as a particularly useful solution to handle sharing issues by exploiting smart contracts, distributed ledgers, or consensus mechanisms. For example, in [193], a blockchain-based decentralized distributed storage and sharing scheme, called the IoTChain model, has been developed to enable data encryption and fine-grained access control. In such a model, attribute-based encryption (ABE) plays the role of granular permission, while the Ethereum blockchain is responsible for controlling access. Instead of relying on TTPs, the work in [194] introduced secure data-sharing systems that relied on key aggregate searchable encryption and blockchain technologies, allowing owners to have all their ability to use, share and manage their data. On the other hand, ABE has also been attractive as it allows data to be safely stored in untrusted storage, for example, physically accessible sensors, hackable publish-subscribe brokers, and third-party cloud servers [195]. In addition to that, a new deoxyribonucleic acid (DNA) cryptosystem for cloud-based IoT infrastructure has been proposed in [196] to hide and encrypt confidential data in an image before storage using a long secret key.

### C. Integrity

Integrity is a security property that means maintaining and ensuring the accuracy and completeness of data and information in IoT environments. Maintaining integrity is essential to ensure that the data and information collected and transmitted by IoT devices are trustworthy and reliable. However, it is still an open challenge because wireless transmission in IoT applications can cause errors or erasures in the data due to many reasons, such as attenuation, distortion,

noise, and storage. Following that, in order to realize the integrity property for data and information, the most important task is to determine three states of information along the path between network nodes: (1) In-state motion, the data information journey is required unchanged from the IoT device to the cloud application, (2) In an idle state, stored information programs must authenticate data information by startup, (3) In state processes, checking integrity is required to perform periodically during operation and even with startup and shutdown. Accordingly, several possible methods to guarantee integrity have been developed for IIoT systems, such as a symmetric cryptographic algorithm, a secure hash algorithm, a message integrity check (MIC), and a checksum and cyclic redundancy check (CRC). However, depending on each application, these will be exploited in different manners.

Wearable sensors have attracted considerable attention in the medical field of IoT due to their ability to allow simple monitoring of human health through biochemical identification [49]. However, their widespread adoption limits the ability of intelligent learning networks to perform multiple tasks and ensure data integrity. To this end, some recent efforts have focused on building automated security systems based on data impact anomaly detection and classification, checking the integrity of electrocardiogram (ECG) data [197], tracing the root cause of integrity compromise [198], and leveraging blockchain to improve the data integrity of food and drug administration (FDA)-approved medical wearables [199].

A smart home can make life more convenient than ever, but in return, it also involves a lot of data integrity concerns due to the complex coexistence of devices. Furthermore, the deployment of a multistep algorithm also results in a high power consumption of the devices [200]. To solve these, the work in [201] proposed a protocol for smart homes based on symmetric cryptography by incorporating the BAN logic and the Scyther tool. On the other hand, research in [202] argues that the use of cryptographic signatures and smart contracts still has vulnerabilities, and the use of smart home blockchain networks can preserve data integrity and robust security in different ways. In this context, [202] proposed four different consensus algorithms, namely concatenated hash transactions (CHT), Merkle hash tree (MHT), even modified MHT, and modified MHT. At the same time, the work in [203] utilized a hyper ledger fabric platform in conjunction with a hyper ledger composer to track the flow data within the smart home system. Meanwhile, the work in [204] relied on the home gateway to aggregate all data information and design a data structure so that the data tag information can not only be efficiently and dynamically stored but also reduce local storage pressure. Recently, the authors of [205] introduced a new secure and trusted blockchain-based onion routing framework, where the anonymity of targets is maintained by storing and tracking the onion nodes' threshold values through the blockchain network while the sensor's data requests are classified as malicious or nonmalicious through LSTM model.

In the smart grid, real-time energy management systems are in charge of control, scheduling, and decision-making tasks
and rely primarily on the state estimation process. Thus, it is prone to a well-coordinated data integrity attack (DIA), causing power outages and disordered generation dispatch. In this context, the work in [206] designed a unified framework of zero-parameter information DIA and countermeasure by carefully looking at the system topology and branch parameters. However, such a system-based model approach may be limited to complex scenarios and presents new challenges in the development of appropriate algorithms. To address this limitation, some recent work is focusing on observation-driven combined data driven system models [207], [208], [209]. For example, the work in [207] designed a general solution with three main steps: (1) determining all possible attack strategies related to dynamic state estimation; (2) formulating the DIA detection problem into a partially observable Markov decision process; and (3) building a DL detection framework, where the LSTM layer is used to extract anomalous state change while the DRL algorithm aims to detect DIA. In [208], a two-stage power state prediction and attack detection framework has been proposed, where the first aim is to focus on power system state prediction, while the second uses a Kullback-Leibler-based binary classifier to detect false data injection attacks. Unlike [208], the work in [209] proposed the voting-based ensemble learning technique with an imbalanced training dataset in the case of full alternating current but limited direct current state estimation information.

However, in the IoT environment, issues such as hardware failures, software corruption, or malicious intrusions can disrupt communication. This often makes it difficult for IoT devices to detect such events in a timely manner. Consequently, ensuring data integrity verification emerges as a paramount challenge in IoT storage security. By introducing a TTP, the work in [210] demonstrated that their approach based on a short signature algorithm can effectively reduce the overhead of the hash function in the signature process. However, the authors of [211] argued that the use of TTPs might increase computational and communication overhead in large-scale IoT data, and therefore introduce a new scheme, named blockchain and bilinear mapping-based data integrity (BB-DIS). In BB-DIS, collected IoT data is divided into shards and homomorphic verifiable tags, and therefore its integrity can be easily overseen by blockchain transactions. To reduce the cost of certificate management, key escrow, or the requirement of a secret channel per user, the work in [212] developed an efficient certificate-based data integrity auditing protocol for cloud-assisted wireless body area networks by fixing the computational cost in tag generation for a data block so that it is independent of the size of the data block. On another front, the work in [213] claimed that only considering storing a single copy in the cloud is inadequate; it would be better to store multiple replicas in the cloud. In order to simplify the authentication with only two hash operations, a provable multicopy integrity auditing scheme was suggested by leveraging the indistinguishable obfuscation technique. In summary, securing integrity checks for cloud services is critical to ensure that reliable IoT data are saved and maintained, where employing blockchain techniques is a great effort and was recently reviewed in [214].

## D. Intrusion

Intrusion into intelligent IoT networks is a security threat related to unauthorized access or attack on IoT devices, data, or networks. Intrusion can jeopardize the confidentiality, integrity, and availability of the IoT system, resulting in a variety of losses such as data theft, denial of service, botnet infection, or malicious. Therefore, in IIoT networks, it is important to have an IDS that can monitor network traffic and detect any anomalous or malicious activities. Besides, the security of the IoT system can be also enhanced with IPS mechanisms in order to achieve real-time protection, reduce false positives, and adapt to dynamic environments. In what follows, we explore one by one the recent IIoT intrusion detection and prevention developments.

1. **IIoT Intrusion Detection System:** The presence of IDS could be a device/software application that monitors any suspicious activities and gives warnings about harmful activity or policy violations. An IDS can be deployed in different locations, such as host-based, network-based, protocol-based, application protocol-based, or hybrid. There are three common detection variants that IDS employs to monitor intrusions:

* **Signature-based detection:** This type of attack can easily detect known cyberattacks by looking for specific patterns (e.g., byte sequences in network traffic or signatures); however, it has difficulty detecting new attacks when no pattern is available.

* **Anomaly-based detection:** This type of security system takes advantage of ML to create a reliable operating model and compares new behavior with the model, thereby enabling the detection of unknown attacks, in part due to the rapid development of anomalies. Compared to traditional signature-based IDSs, this approach shows better generalized properties as it is trained according to specific application and hardware configurations. However, they also yield more false positives.

* **Reputation-based detection:** Identify potential cyber threats by reputation score.

Although the use of IDS does not stop the attack by itself, it can be configured quickly and easily to detect known and unknown threats and does not affect the performance or availability of the network. Thus, the functionality of IDS has been exploited and developed for distinct IIoT architectures.

In decentralized IDSs, the work in [215] merged SVM-supervised ML into dedicated IDS agents to monitor and alert the vicinity whenever a malicious node occurs. To fine-tune the learning network's ability to spatiotemporal representations, the authors of [216] produced semi-supervised DL methods through a multi-scale residual temporal convolutional module. In addition, the work in [217] proposed a novel cluster-driven ensemble learning algorithm to identify anomaly data in the sensor measurement and control signal. Based on the available data on edge devices, the FL framework has been used efficiently to detect IoT network incursions, especially when incorporating RL [218], AE model [219], and DNN [220]. Remarkably, FL-based RL showed superiority over the SVM-steered IDS with accuracy and detection rates of 0.985 and
96.5%, respectively. Furthermore, FLs are particularly bene-
ficial for softwarization of networks to achieve an accuracy
of 84.32% and detection rates up to 83.10% [221]. To
improve learning networks with decentralized heterogeneous
environments, some recent efforts have suggested the use of
adaptive learning IDS, for example, RL in [222] to classify the
monitored resource consumption parameters or segmented-FL
in [223] to aggregate a global model from shared parameter
among the workers and automatic segmentation of workers.
However, collaborative development of IDSs in these envi-
ronments can cause conflicts of interest. To this end, the
work in [224] designed decentralized Markov interaction-
distribution processes to initiate the information interaction
among agents. At the same time, the work in [225] looked
at the use of BCT to establish distributed trust between
participants. On the contrary, the work in [226] formulated
the problem of security, decentralization, and differential
privacy into three building blocks. First, a key exchange
protocol is responsible for securing the communicated weights
among all peers. Second, a differentially private gradient
exchange scheme aims to counteract privacy leaks. Last, a
decentralized FL is applied to mitigate the failure/attack risk
involving the aggregation server. Consequently, this approach
can achieve performance comparable to centralized learning
(approximately 94.37%) and outperforms the only FL-based
approach (93.91%) in accuracy. Compared to its FL-based
IDS counterparts in terms of F1 score, recall, and precision,
it, respectively, shows overall performance improvements of
12%, 13%, and 9%.

In distributed IDSs, the work in [227] presented a security
mechanism and a guarantee of true operation based on an
exact greedy boosting ensemble method for fog computing
nodes. Through the IoTID20 dataset for smart cities, the
results achieved by the modeling knowledge of the proposed
XGBoost algorithm provide superior attack classification
ability compared to modern ML techniques in distinguish-
ing normal and anomalies; specifically, the adoption of the
XGBoost algorithm for binary and multiclassifications has the
accuracy of 98% and 99%, respectively. Similarly, the work
in [228] deployed a Raspberry Pi cluster on a local area
network and demonstrated that the XGBoost can achieve a
recall of 89% in ADFA-LD datasets, a predicted inference
time of 130 ms compared to the cloud with 735 ms, and an
estimated running cost of 201 Indian rupees/month against
the cloud cost of 2051 Indian rupees/month. To reduce the
new attack detection time and complexity, the work in [229]
suggested using only adaptive online ML algorithms for
cooperative network IDSs. Meanwhile, the work in [230]
proposed a deep-IFS framework to address current scalability
and performance limitations in handling big IIoT traffic data.
Experimenting with BoT-IoT datasets, this framework not only
gains significant robust performance for binary classification
(accuracy: 99.75; F1-score: 98.14; AUC: 99.98, training time:
135.6s) but also the multiclass scenarios (accuracy: 99.77;
precision: 99.99; recall: 99.77; F1-measure: 99.88, training
time: 184.8s). In [231], a novel DeepAuditor approach has
been designed for multiple IoT devices through power auditing
to detect malicious behavior with up to 98.9% accuracy. To do

this, in DeepAuditor, a distributed CNN classifier focuses on
online inference in a laboratory setting, while encapsulated HE
and sliding-window protocols are used against data leakage
and reduce network redundancy.

2) IIoT Intrusion Prevention System: An IPS is a security mechanism that not only detects intrusions but also prevents or blocks them from affecting IoT systems. There are different modes in which IPS can work, such as inline, tap, or span, depending on how it interacts with the network traffic. Based on the approach that IPS systems exploit to scan network traffic in one or more detecting times, IPS can be distinguished as follows:

*   **Signature-based detection:** This type mainly analyzes network packets and then compares them with pre-configured and predetermined attack patterns known as signatures.

*   **Statistical anomaly-based detection:** This method uses an established baseline to determine whether a pattern observed in network traffic is abnormal or normal. For example, the baseline describes how much bandwidth is utilized and what protocols are used; if sample detection deviates from this baseline, it is flagged as an anomaly. This kind of protection is useful for detecting new threats, but it also produces false positives when the legitimate user uses resources that overwhelm the baseline or when the baselines are not properly established.

*   **Stateful protocol analysis detection:** This approach detects protocol state violations by comparing observed events to predefined profiles of commonly recognized definitions of benign activity.

Although the realization of an IPS has significant hurdles (i.e., resource constraints, scalability concerns, and compatibility issues with various IoT protocols and devices), they may improve the security of an IoT system by offering real-time protection, decreasing false positives, and responding to a changing environment. Compared to IDS, an IPS can be envisaged as an extension of an IDS as it stops the potential attack from happening rather than just reporting it. For this reason, IPSs can be considered the next step after IDS in the progression of security technology.

In an effort to achieve decentralized IPSs, the authors of [232] suggested the use of the multi-feature extraction (MFE) process in cloud computing and the MFE-extreme learning machine (ELM) algorithm to detect and discover network intrusions to cloud nodes. Which, ELM directly learns to use the least squares method instead of iterating to adjust the neural network weights and biases of the nodes in the hidden layer, thus gaining a faster learning speed and reducing the risk of overfitting. In [233], a novel DNN-based multistage hybrid intrusion detection and prevention system framework was proposed to sequentially address the limitations of each learning stage. Initially, conventional DNN detection models learn, train, and validate the learning dataset. Misclassified or classified samples with low confidence in decision are then used to create a new dataset for next-stage DNN training. Finally, a collaborative IPS capable of emergency response will automatically mitigate attacks and detect anomalies. The numerical results confirm that such a framework achieved
excellent performance in the WUSTL-IIOT-2018 dataset, with a detection accuracy of 99.99%, a false alarm rate $\le 0.01\%$, and an undetected rate of 0.18% (that is, false negative rate).

In distributed IPSs, to prevent bot intrusion and control the activities of the system, the work in [234] applied a co-relation-based subset evaluation method combined with a novel cascade forward back propagation neural network model. Specifically, this approach evaluates each feature's ability to predict the class variable and measures the degree of redundancy between the features. Here, the subset is generated by selecting the variables having a high correlation with the attack type and a low correlation among other features. Later, extraneous and redundant features were removed to retain the most prominent features of the training data set. Through experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows, this approach shows 100% accuracy on testing of the subset and 97.3% accuracy for the full dataset. Similarly, the work in [235] also proposed a feature selection based on a genetic algorithm wrapper combined with the Nave Bayes model to detect anomalies to reduce time complexity by removing unnecessary attributes while improving accurate predictions thanks to the security laboratory knowledge discovery dataset. In [236], a new attack mitigation module was integrated into the fog nodes to perform IDSs based on supervised learning. When this module is activated, a drop rule is applied to the MAC-IP address pair of the IoT device that sent the malicious packets. To avoid blocking legitimate traffic, a random timer is set before removing the drop rule. The captured traffic is analyzed by the admin and used to update the model if needed. If the mitigation module receives uncertain input, denoted by a question mark, a drop rule is set for a shorter random period. The administrator reviews the traffic to determine whether it is an attack and updates the model accordingly when necessary.

### E. Countermeasures

1) **Unreliable Communication:** In the IoT network, countless systems coexist and interact with each other to support human activities: home automation, grids, agriculture, transportation, and healthcare services. However, such complex environments generate new security threats related to data breaches, identity theft, and unauthorized access to confidential information. To address such challenges, researchers have developed corresponding countermeasures using combinations of cryptosystems with new technologies and strategies. Researchers in [193] introduce an attribute-based access control and AES-128 encryption schemes to better encrypt IoT streams before uploading to the interplanetary file system. The elliptic curve Diffie-Hellman key exchange protocol, a secure method of distributing private keys, enables data users to access transaction details from the Ethereum blockchain and retrieve their private keys if they forget them.

For the Internet of medical things aspect, the work in [180] uses a DL-based approach to transfer the medical image into two image domains, which include the original medical image domain and the target domain (regarded as 'hidden factors'

to guide the learning model). DL-based encryption design consists of two components: a generator and a discriminator. The generator components create an image that resembles the target domain, while the discriminator component encourages the former to create images identical to those in the target domain by recognizing the generated images. In this way, the original medical image can be transformed into ciphertext in the target domain. Accordingly, these ciphertext images can be secure on the storage server from any unauthorized access.

For the aspects of the wireless sensor network (WSN), the work in [181] overcomes the unpredictable nature of sensors and wireless channels using a two-phase process. In the first phase, DRL is used to identify energy-efficient and fault-tolerant routes through interactions with the environment. In the second phase, an AES cryptography-based deterministic procedure is considered to produce cipher blocks. The encryption process includes two steps: i) a pseudorandom code, called a keystream, is generated based on the function F given as

$$F = k_s \in \{0, 1\}^n + \beta,$$

where n represents the size of the individual keystream while $\beta$ implies the block of bits to initiate the starting variable. ii) the cluster head j aggregates the data items D and uses AES to perform an arithmetic modular operation on D and $k_s$ to produce a cipher block $C_j$, which cannot be repeated twice for data encryption.

Nevertheless, the symmetric solutions used above are still resource-intensive, leaving a significant need for lightweight cryptography for resource-constrained devices. To this end, four algorithms defined as the latest ISO/IEC 29192 standards (SPECK, SIMON, PRESENT, and CLEFIA) [237] should be considered when designing learning methods. However, the selection of these algorithms is contingent on the hardware specifications of the IoT device in use, as well as the desired degree of security. To be specific, SIMON is a Feistel network block cipher optimized for hardware-based implementations, supporting key sizes ranging from 64 to 256 bits and block sizes from 32 to 128 bits. Conversely, SPECK is tailored for software-based implementations with a low memory footprint due to its code's operations, enabling key sizes ranging from 128 to 256 bits and a block size of 128 bits. Meanwhile, CLEFIA is used for both hardware and software implementations, supporting multiple key sizes ranging from 128 to 256 bits and accepting a 128-bit plaintext block. However, it is prone to differential attacks. PRESENT, on the other hand, is a hardware-oriented block cipher belonging to the substitution-permutation network family of algorithms for various applications that do not require high levels of security, with a block size of 64 bits and key sizes of 80 and 128 bits. Although it is susceptible to various attacks such as birthday attacks, such risks can be well solved by encrypting large datasets.

2) **Unreliable Data Aggregation:** Data generated by the multitude of local IoT devices need to be collected by cloud or edge servers to be used in training intelligent models. Despite being encrypted through various security methods before communication, aggregating data without additional protection can still result in vulnerabilities (e.g., lost data, modified
data, and leaked private identity/individual information) when processing the sharing and querying tasks that impede the development of trustworthy IIoT networks. Driven by these issues, recent studies specify countermeasures by directly encrypting collected IoT data during the period of aggregation processes. Researchers in [182] proposed a novel encryption-based FL solution, where Shamir Secret Sharing (SSS) and the SSS homomorphism are used to back up the secret data of users whenever a user breaks down and to support two sets of shares, respectively. In particular, the communication between the user and the server is executed in 6 rounds as follows. *Setup*: the data are prepared. *Keys Distribution*: each user generates a pair of public keys and shares a secret with the other users. *Keys Sharing*: the input data is encrypted by the users using secret sharing homomorphisms and then transferred to the server as ciphertext. *Model Collection*: both the server and users confirm the set of active users. *Message Check*: the user encrypts the input data and sends them to the server as ciphertext. *Unmasking*: the server calculates an aggregated plaintext based on the collected shares and ciphertexts and stops the aggregation process if the number of receiving messages is less than the predefined number before timeout. Then, a global model with the masked trained model on the server will be returned to users for usage and retraining.

On the other hand, the authors of [238] suggested ultra-lightweight data aggregation frameworks with DL protection that do not require the maintenance of a secret key to communicate with the aggregator by defining a two-phase operation: enrollment and data aggregation. In the first phase, the endpoint gateways send an enrollment request to the area aggregator through a secure channel. The area aggregator first generates a set of challenge requests to the sensor via end-point gateways to collect a sufficient number of challenge-response pairs. The internal characteristics of the original physical unclonable function (PUF) are then estimated using an ML-based training PUF model built in naive Bayes/linear regression algorithms. In addition, a preselection and filtering mechanism is also considered to eliminate the impact of noisy data caused by the response of a PUF. In the end, the area aggregator generates a unique *id* for the sensor and stores it along with the trained PUF model in its secure non-volatile memory while sending a copy version to the end-point gateway. The latter will then ask the sensor to disable the PUF to prevent illegal access. In the second phase, for a particular session, a sensor randomly generates a pair of the challenge and timestamps used as input of the PUF response before encrypting them and the data collected by this session with the PUF key. Upon receiving the encrypted message from the sensor, the end-point gateway attaches this message along with *id* and then sends it to the area aggregator. For each received message, the aggregator loads the trained PUF model and generates the PUF key to decrypt this message, as well as check the validity of the timestamp and challenge. When doing the same for the other message, the aggregator can get and calculate the sum of all data reads.

However, the practical designs of the DL approaches in [182], [238] have not been focused on and are still limited

by interpretability problems, requirements for large amounts of data, hard-to-design and tune parameters, and a high complexity of computation. As such, to increase the diversity of the model to improve the ability of representation and feature extraction while maintaining the original merits of the traditional model, it should be further considered with the feature of a lightweight deep model [239] by constructing the hierarchical and cascaded model structure. To achieve this, the partial least squares approach is considered in order to model the association between the observed and predicted variables using latent variables when dealing with the problems of small sample capacity, high variable dimension, or bad multiple correlations.

3) *Unreliable Data Storage*: Secure data storage is always a competitive area to prevent critical problems related to secret key leakage or the replacement of sensitive data with files tampered with by attackers. Consequently, many research efforts have focused on integrating keys into data during the aggregation phase by leveraging conventional encryption mechanisms and/or combining them with BCT. However, using such cryptosystems typically requires enormous computing power in the key generation algorithms, and the dissemination of such public keys still presents risks, especially with collecting and aggregating big IoT data. These certainly affect the ability to store data securely. In this context, it is preferred to use DNA-based cryptosystems along with advanced AI technologies that allow encryption and decryption algorithms executed with simple operations, thus improving significant computational resources [240]. Moreover, when dealing with untrusted third parties, masking encrypted confidential data in an image before storage with a DNA cryptosystem designed in [196] and distributing them with multiple replicas on untrusted third parties/clouds as suggested in [213] could be promising solutions not only to secure data storage but also to improve IoT network resources.

4) *Unreliable Learning*: Decentralized and distributed IoT architectures can ease the burden on central servers/clouds by allocating resources and minimizing overheads more efficiently than centralized ones. However, these also pose some challenges in creating reliable learning frameworks. This problem stems from the huge and diverse IoT data generated by various intelligent IoT applications and devices along with the presence of a multitier IoT network that can coexist with other networks. Therefore, it is difficult to build standard rules and a unified collaborative learning framework for data sharing and training among parties. Moreover, this becomes more difficult when some parties/clients are untrustworthy, as they can manipulate the outputs sent to the server and influence the training process in negative ways. In this context, exploiting FL frameworks, aware of unreliable agents [241], as well as combined IDP strategies [216], [223], [226] to reduce the risk of failure/attack on the aggregation server and/or combine with IPS [233], [234], [236] to solve the computational overhead in performing the training, detection, and recognition process; the reliability of learning phases can be improved.

5) *Over-the-Air Firmware Updates*: For IIoT networks, over-the-air firmware updates by wireless capabilities are
regarded as a common, flexible, and efficient approach that enables updating new features, fixing bugs, supporting protocols, and resolving known security vulnerabilities. However, updating a large number of IoT devices like this appears to open the door for potential attackers to compromise the availability and integrity of firmware updates without access. However, securing IoT devices becomes challenging due to limited interaction abilities, battery power, sleep mode, number of devices, and different properties such as system-on-chip, manufacturers, providers, and communication protocols. These make traditional security mechanisms difficult to adopt. To protect the integrity of firmware update processes, it is preferred to use a blockchain framework with smart contracts in [242]. The framework involves six steps: 1) The vendor service generates a new blockchain transaction ID that includes the information of the target IoT device and the SHA1 hash of the new firmware update; 2) This firmware update will be sent to the target IoT device with ID; 3) After receiving the firmware update binary, the IoT device calculates the SHA1 hash; 4) The IoT device validates the update in the distributed ledger with the calculated SHA1 hash and transaction ID; 5) if validation succeeds, the IoT device applies the firmware update and sends a status update to the blockchain; otherwise, the firmware update process is aborted and a failure notice is recorded; and 6) The vendor service queries the blockchain to collect updated statistics to determine further actions.

6) On-Device AI: The growth of IIoT is expected to be widespread in both the vertical and horizontal domains. In that sense, IoT devices will be equipped with microlearning models capable of performing various tasks such as OCR, face recognition, liveness detection, identification card and bank card recognition, and translation. This approach helps IIoT networks avoid sending users' private data to the cloud, eliminates the need for a network connection, and saves the latency of back-and-forth communication. However, deploying a large number of AIs on a device without proper control raises significant security concerns regarding model privacy. Hence, it is imperative to safeguard model privacy while accessing untrusted AI accelerators for essential tasks. In this context, there are two highly recommended methods for secure on-device learning models: ShadowNet [243] and MirrorNet [244]. ShadowNet is an exceptional framework developed based on CNNs that transforms the weights of linear layers before outsourcing them to the untrusted world and then restores the results inside the trusted execution environment (TEE). This allows the heavy linear layers of the model to be securely outsourced for acceleration without leaking the model weights. On the other hand, MirrorNet is an excellent alternative that uses the input DNN model as a backbone network and connects a lightweight mirror network to it, which is stored in a secure world. This approach effectively eliminates the vulnerability to model extraction and meets the computational and storage limitations of the TEE. The lightweight network is a crucial component of the entire model and allows authorized users to perform high-performance inference combined with the backbone network.

## V. PRIVACY ISSUES IN IIoT

The IIoT infrastructure for computation and communication must not only be efficient and reliable but also be trustworthy. While the efficiency of IIoT has been widely studied, critical privacy concerns have received less attention, and the phase of information collected, transmitted and analyzed, as well as the ML training, is the most vulnerable. Private information of interest might encompass payload data acquired by sensors and IIoT devices, which are then transmitted through the network to a centralized processing server or retransmitted to other IIoT devices. For instance, when a medical IoT transmits monitoring data to a remote hospital or doctor's office, sensitive information such as a patient's blood pressure, sugar level, and other vital signs becomes a significant privacy concern. At the same time, privacy concerns may also arise for context information, e.g., the location of a sensor, and for the parameters of ML models fed with the data from the sensors. Given the importance of information, it requires appropriate protection from adversaries, and users should be fully aware of the processing of their private data. While in the previous section (Section IV) we focused on measures taken to protect IIoT systems, data, and resources from unauthorized access, in this section we focus on how to keep personal data confidential and limit its collection, use, and disclosure without consent, where confidentiality is only one aspect. As such, we will describe the definition of privacy (Section V-A), attacks on this set of information, that is, data (Section V-B), location (Section V-C), and models (Section V-D), and possible countermeasures (Section V-E). Finally, we summarize the main trend for preservation of privacy in IIoT in Section V-F.

### A. Privacy in IoT: Concepts and Principles

Although privacy is a very broad and multifaceted concept, privacy in IIoT refers mainly to the exposure of information from smart objects to the outside world [245]. Therefore, it becomes crucial to acknowledge and regulate the utilization and distribution of personal information by entities beyond individual control. Privacy threats are commonly among the primary concerns of users and can significantly impact the adoption of new technologies, particularly in the context of IIoT, where devices make use of personal information.

The work in [246] highlighted that privacy must be approached from a design thinking perspective. In particular, privacy must be incorporated by default into networked data systems and technologies and become integral to organizational priorities, project objectives, design processes, and planning operations. Protection of privacy in the modern era should follow the principles summarized in Table VI. From these principles emerges how privacy by design requires architects and operators to keep the interests of the individual by offering measures such as strong privacy defaults, appropriate notice, and user-friendly options.

In addition to these principles, [247] also includes six more principles:

1) *Data minimization:* To proactively prevent privacy risks, it is essential to systematically minimize the amount of data collected and processed. As a result, the
<table><caption>TABLE VI PRIVACY PRINCIPLES IN IIoT</caption><thead><tr><td>Principle</td><td>Explanation</td></tr></thead><tbody><tr><td>Privacy included by design</td><td>Privacy is not an add-on feature but an intrinsic element deeply ingrained within the fundamental functionality of a computer system. It involves seamless integration with information technology, operations, and architecture, encompassing a holistic, integrated, and innovative approach.</td></tr><tr><td>Privacy is a default setting</td><td>Users do not need to proactively take steps to protect their privacy. Instead, fair information practices will notify them about the desired specifications, limitations on data collection, restrictions on use, data retention policies, and disclosure practices.</td></tr><tr><td>Proactive not reactive, preventative not remedial</td><td>The objective of this setup is to deter privacy intrusion by acknowledging the significance and advantages of maintaining routine. This involves having well-defined commitments at the highest level, clear communication of privacy commitments between the user community and stakeholders, and established mechanisms for identifying and addressing inadequate privacy practices.</td></tr><tr><td>Full functionality — positive-sum</td><td>It employs a win-win approach, avoiding compromises between different goals, and strives to achieve all desired objectives (e.g., confidentiality and functionality) in a user-centric manner.</td></tr><tr><td>End-to-end security — full lifecycle protection</td><td>Privacy are seamlessly integrated into IIoT systems right from the moment the first record is captured and throughout the data's lifecycle. This ensures that information is securely stored, processed, and appropriately disposed of at the end of the process.</td></tr><tr><td>Visibility and transparency — keep it open</td><td>It assures all stakeholders that information is managed by stated commitments and goals, and its components are transparently visible to ensure accountability and trustworthiness.</td></tr></tbody></table>

development of software, information, and communication technologies and systems should start with non-identifiable interactions and processes. Wherever feasible, consistently identifiable and observable personal information should be limited to the minimum necessary.

2) **Informed consent:** The terms are presented in a clear, relevant, and transparent manner, enabling users to make informed decisions about sharing certain information, unless legally required. The level of data confidentiality determines the quality of consent needed from users.

3) **Transparency:** Users should be provided with an overview of how their data will be processed and used during its utilization.

4) **Verifiable preventive protection:** Implement security measures that may include verifiable measures.

5) **Accuracy:** Maintain accurate, complete, and up-to-date personal information necessary to achieve the stated objectives.

6) **Possibility to withdraw consent:** Allow users to revoke their consent at any time and provide them with the option to delete any shared information.

Although these principles may not guarantee the absolute inviolability of users' "personal lives", failing to implement privacy-by-design principles leaves the IIoT system vulnerable to risks such as eavesdropping, spoofing, RF jamming, and others reviewed in the following.

In the IIoT environment, the collection, use, and exchange of user data are widespread practices. Reference [248] reviews and identifies the most common threats to IoT privacy.

(a) **Identification:** An attacker can link identifiers, e.g., names and addresses, to specific individuals. In traditional centralized services, a large amount of information is concentrated in a central location beyond the control of the individuals. However, in the context of IoT, especially

in IIoT, the stages of interaction and data collection become crucial, as the increased frequency of interactions amplifies the risk of identification.

(b) **Location and tracking:** The location of users can be determined using various tools, e.g., GPS, Internet traffic, and the location of smartphones. Instances of privacy breaches have been detected, such as GPS tracking, disclosure of personal details such as health conditions, and unease related to surveillance or oversight.

(c) **Profiling:** Organizations gather relevant information through interactions with various profiles and data sources to customize electronic commerce. As IIoT evolves, not only does the volume of data grow exponentially every day, but the nature of the data also undergoes qualitative changes, as it now includes previously inaccessible aspects of an individual's personal life.

(d) **Interactions and presentations:** The possibly large number of smart items and new ways of communicating between the system and the customer threatens confidentiality and personal information, as the majority of such mechanisms utilized for user interaction and feedback are inherently public in nature, which can potentially compromise individual privacy.

(e) **Life cycle transitions:** After IIoT products are sold and eventually discarded, it has been confirmed that smart devices retain a significant amount of historical data accumulated throughout their lifetime that is not deleted during transfer of ownership.

(f) **Inventory attacks:** An attacker can exploit unauthorized access to personal items and features to gather information and then use inventory data to identify and destroy properties.

(g) **Linking connections:** The interconnections between various systems in the recent IIoT ecosystem make unauthorized access and intrusion of personal data a significant and serious threat.
IIoT users are confounded by the above threats, partic-
ularly when they become aware that these devices collect
and transmit personal data without their full knowledge. In
addition, the new scenario of IIoT poses new challenges
that are unique and new compared to the traditional IoT
environment. Now we review the threats that can occur at the
data level (Section V-B), location level (Section V-C), model
level (Section V-D), and then some possible countermeasures
(Section V-E).

B. Data Privacy Leakage

Given the vast range of IIoT applications, e.g., remote patient monitoring, energy consumption control, traffic control, smart parking system, and inventory management; for all of them, users require the protection of their personal information. The general defensive approach uses encryption and authentication to protect privacy. However, because of such a wide range, the literature presents diversified attempts to address such an issue and ensure user data privacy. In what follows, we differentiate these techniques, starting with general approaches to protect user privacy, continuing with the challenges in more specific use cases such as smart grids and UAVs, and concluding with the data streams in IIoT scenarios.

1) User Privacy: A significant data privacy concern involves the exposure of personal user information during its transmission over the Internet [247], [249]. For instance, let's consider a scenario where a consumer named Bob makes a purchase using his credit card for an item with an RFID tag. An attacker could link Bob's personal information (known to cloud service providers) to the specific purchased object. Such leakage of user information poses privacy threats, including tracking, localization, and personalization risks. Additionally, if Bob owns a set of interconnected objects, adversaries who can differentiate ownership of certain items might also be able to estimate ownership of the remaining objects. These examples are issues that allow user profiling and tracking. Moreover, smartphones and other mobile devices connected to the Internet can reveal a user's geographical location, potentially compromising privacy. Therefore, it appears that users have varying levels of privacy awareness and concern, as information disclosure can occur to different degrees.

IIoT networks can include tens of thousands to millions
of devices, each with a unique set of characteristics such
as resource limitations, mobility, scalability, autonomy, and
interoperability. As a result, depending on the IIoT applica-
tions used, different privacy concerns arise. In the domain
of smart healthcare, the growing accessibility and prevalence
of personal health records on the Internet (e.g., via wearable
health monitors for blood pressure and heart rate, smart
apparel, and fitness trackers) can also give rise to significant
privacy concerns. In June 2015, a major privacy-violation
attack took place, in which malware infiltrated blood gas
analyzers to exploit hospital networks and illicitly obtain
confidential data [250]. Several international regulatory laws
designed to limit data access and safeguard the privacy of
medical data make it even more important to protect elec-
tronic health records. For example, in the USA, the Health

Insurance Portability and Accountability Act (HIPAA)<fnref n="2" /> and in the European Union, the General Data Protection Regulation (GDPR)<fnref n="3" /> completely rewrite the data management policy. As a result, valuable data are frequently kept on site, for example, specific hospitals, and the analysis through ML and DL models takes advantage of FL as a privacy-preserving method [49] (see Section II-C).

In smart homes, where users can remotely control, monitor, and measure the power consumption of household appliances through the Internet, there is a potential threat to user privacy. With the use of RFID and sensing technologies, residents can monitor the conditions of their smart home and track objects [251]. Intelligent adversaries can easily infer information about residential behavioral patterns (such as when residents are at home, away from home, or sleeping) if they eavesdrop on and collect timestamps of data transmissions over wireless channels. Attackers can also examine the transmission patterns and infer approximations of the internal layout of the home by analyzing the radio wave patterns and identifiers that are specific to sensors and RFID tags.

In particular, the communication channel in IIoT can lead to problems such as eavesdropping on adversaries. Private data transmission can be ensured by private secure communication, particularly in wireless environments. Sensitive data may be exposed to adversaries if communication channels are made available. Privacy in communication is a challenging topic because there are so many different communication standards, such as Wi-Fi, Bluetooth, ZigBee, Z-Wave, and LoRaWAN.

Finally, given the global nature of the IIoT, we can mention
how national-level regulations are unacceptable for privacy.
Cross-border and compliant with international law, the private
sector should also enhance an adequate legal framework.
Although state laws are a more complex and expensive option
for protecting privacy than self-regulation, IIoT applications'
extensive deployments of heterogeneous networks make self-
regulation insufficient. The global distribution and marketing
of IoT products, their durability, integration with ubiquitous
environments, and the complexity of technological advances
present the most difficult regulatory challenges.

2) *Smart Grid*: The Smart Grid is an electronic-controlled electrical system that links consumers, power producers, and distributors using information and communication technologies, where customers can be generators or producers and add their energy to the grid (prosumers) [252]. The architecture provides customers with tools for energy management, improves reliability, resiliency, and power quality, and allows them to use cutting-edge technologies such as renewable energy, energy storage, and electric vehicles. Smart meters also facilitate information flow, enabling two-way communication between customers and utilities, stakeholders, and all other operators.

In managing energy consumption within smart grids, con-
sumers share detailed information about their daily power
consumption [253]. This information can be leveraged to

<footnote n="2">²https://www.cdc.gov/phlp/publications/topic/hipaa.html</footnote>

<footnote n="3">³https://gdpr-info.eu/issues/data-protection-officer/</footnote>
reveal their habits and behaviors, potentially leading to inva-
sion of privacy. Unfortunately, user data regarding electricity
consumption can be intercepted from any point on the Internet.
As customers have less control over the data they provide
to utilities, users can have concerns about unauthorized use
and disclosure of personal data, data leaks or spoofing via
hacking, and inferences made from new data types aggregated
with other personal data. Reference [254] presents a new
risk assessment methodology for smart grids, starting with
component evaluation, for example meters, then subsystem
evaluation, and finally with a complete system evaluation.
To this end, in [255], it is proposed an innovative proto-
col is proposed that protects users' privacy, confidentiality,
and integrity while sharing necessary information. They also
suggested a new distributed multiparty computation (MPC)
protocol that is clustering-based. The authors derive the con-
ditions under which the smart grid can enforce cooperation
among users and prevent dishonest consumption declarations
by taking into account repeated interaction between honest
and dishonest users. In [256], it has been shown that in a
hybrid architecture comprising an IEEE 802.11s mesh-based
smart meter network and an LTE-based wide area network for
smart meter data collection, it is feasible to employ unique
pseudonyms to translate network addresses for smart meters.
With this approach, the privacy of the consumer can be ensured
because the IP addresses of smart meters remain undisclosed
to utility companies. Finally, the authors of [257] present
a protocol that protects privacy between smart meters and
energy suppliers. They adopted a homomorphic ECC-based
encryption method to effectively protect the data collected by
smart devices.

3) *Unmanned Aerial Vehicles*: Vehicular ad hoc networks (VANETs) play an important role in IoT and IIoT through intelligent transportation. VANETs integrate an onboard unit (OBU) as an IIoT sensing layer node into the vehicle system [258]. This node interacts with other peer vehicles and the infrastructure along the roadside. Therefore, to enable security and privacy in VANETs, two essential requirements are to establish secure communication links and provide authentication. To ensure user privacy protection to the same extent as identity and location protection, the OBU requires additional modules to support information security.

However, in addition to traditional data privacy issues, in
this case, another possibility of privacy disclosure arises when
aerial photos captured by UAVs are sent to the Ground Control
Station (GCS). These photos often contain private information
such as location and shooting time. For example, photos
taken with DJI™ equipment, such as Mavic and Phantom4
pro, are saved in JPEG format. Invisible information such as
image information (manufacturer, size, etc.), camera recording
information (ISO, white balance, saturation, sharpness, etc.)
and GPS (shooting longitude, latitude, altitude) are also
included in these images [259]. Camera manufacturers add
invisible information to the header file of the JPEG photo
to better describe and illustrate the photo. Even after image
modifications, such as blur, mosaic, drawing, and watermark,
the header file can still convey the information of GPS and
shooting time via the reader. The study in [259] found that

after image compression, most of the header file information
is missing, suggesting an interesting way to solve privacy
issues.

In [260], a BCT-based solution is proposed to improve the security and privacy of UAVs, or IoT in general. Technical information about UAV instructions (or devices), authentication, integrity, and UAV reactions is stored on a cloud platform, where the Pentatope-based ECC encryption method and SHA are used to ensure privacy in data storage. The data is later stored on an Ethereum-based public blockchain to enable seamless BCT transactions. The system also uses the Ganache platform for data protection and privacy. Other simpler blockchain-based approaches, as in [261], use identity-based encryption (IBE) and lightweight cryptography techniques to achieve data privacy. This specific solution is general enough that it can be used to orchestrate and govern the delivery of any digital asset or content, including streamed video and audio, which is a good fit for UAV-generated content.

4) *Data Collection and Processing in IIoT*: Due to their intrinsic data-centric perspective, IIoT applications present three critical issues: scalability, distributed processing, and real-time analytics. *Scalability* matters for IIoT applications that contain numerous smart objects or manage biometric data that must be collected, processed, stored and then published. The *distributed* nature of IIoT processing introduces unprecedented challenges related to privacy throughout the *real-time* learning process, along with liability for data breaches (i.e., the release of secure information to distrustful entities) and different levels of data quality. While collecting large sets of raw data, it is challenging to balance the privacy preservation in data cleaning and the intentional reduction of data quality and original purpose without losing information needed for data processing and analysis (e.g., via ML algorithms). The most important privacy concerns in the context of IIoT applications are the gathering, sharing, and transmission of sensitive data related to people. Privacy preservation over high-dimensional datasets is also associated with computational and theoretical constraints. Records in a given dataset should be handled differently for anonymization purposes because cooperative users and individuals have different privacy restrictions. The data collected could be used and published for purposes unrelated to the original goal without the user's consent.

To solve the problem of centralized (private) data storage,
which broadens the surface of attack of data leakage, FL
proposes to store user-related information locally and share
only model-related information [262]. On the one hand, in
FL, sharing locally trained model information, e.g., param-
eters and gradients, allows sufficient knowledge for model
training while preserving data privacy to a certain degree,
since sensitive information remains undisclosed and traffic
can be encrypted [263]. On the other hand, ensuring the
algorithm's optimal performance without compromising user
privacy requires extra effort. Even if data are anonymized, col-
lecting a few data attributes might still enable, for instance, the
reidentification of patients [264]. We discuss in Section V-E
the principal countermeasures to protect user privacy in FL-
empowered IIoT and the simple ML-based learning process in
IIoT.