# A Survey on Information-Centric Networking with Cloud Internet of Things and Artificial Intelligence

Authors: Farhan Ahmed Karim; Azana Hafizah Mohd Aman; Rosilah Hassan; Kashif Nisar
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The Internet is evolving, and data is a critical component of today’s Internet. People are more interested in data than data location. An information-centric network (ICN) uses this idea and makes data, instead of host addresses, an integral component. Another essential topic in the contemporary period is cloud or edge computing, as well as the Internet of Things (IoT) and Artificial Intelligence (AI), which becomes even more critical when combined with ICN. We initially rate the configuration of ICN with cloud or edge IoT and AI (ICN-CIoT-AI) in this study so that readers may learn about the latest trends and merging of ICN-CIoT-AI. As data rates rise and the Internet becomes a requirement for any technology, we require IoT settings in which data can be cached locally, which is possible when ICN collaborates with cloud or edge computing. To make this arrangement more intelligent, we require AI, and machine learning algorithms can help to overcome many obstacles. In this paper, we first discuss ICN, its deployment, and its unique features that distinguish it from its archrival TCP/IP. We then present the most recent research on ICN-CIoT-AI and provide a comprehensive analysis of this domain in terms of technology, AI/ML domain, IoT, and cloud technology. The study framework, simulation software, and results achieved by the researchers are also listed. Finally, we explore three broad categories of open issues and challenges raised by the researchers: security, performance, and in-network caching. We also exhibit the technologies that were employed in the study.


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## Body

## 1. Introduction

The Internet is essential at present. Doing daily activities without it is almost impossible. As the number of users increases every day, the load on the Internet also increases. In 2010, the Internet Protocol (IP) was supported by approximately two billion hosts and approximately 300,000 routers in its passage [1]. With such a heavy load, continuing with the current IP architecture in the near future is difficult because the data rate of media is also increasing. YouTube usage more than tripled from 2014 to 2016; users uploaded 400 hours of new videos each minute of every day in 2017, and users watched 4,146,600 videos every minute [2]. In such a case, users are not concerned about content location but the content itself. This change reflects the limit of the current Internet architecture and opens a new horizon for the Internetworking world.Evidently, the next-generation Internet will be all about speed, data rate, and heavy data. As discussed, the data rate is continuously increasing and so is the load on links. Information-centric network (ICN) architecture addresses these changes, and ICN-based projects promise faster data communication than its rival IP architecture and other network approaches because of its in-network caching capability. However, the contemporary world requires speed because consumer load is increasing every day. People have left televisions and shifted their interest to the Internet. People use their mobile phones where they can easily find world news with a single touch; the same is the case with entertainment. ICN data are distributed in a cost-effective and scalable manner. ICN is meant to be a suitable architecture to serve IoT networks, given the increasing development of IoT traffic.IoT is another prominent technology assistant which is the linking platform of massive heterogeneous network frameworks and systems in various communication patterns, such as human-to-human, human-to-thing, and thing-to-thing. IoT is a domain in which physical objects are continually connected to build an information network with the explicit objective of providing sophisticated and intelligent services to users [3–5]. Likewise, cloud or edge computing facilitates the use of IoT by assisting smart devices such as smart homes and smart hospitals. Given that these vast numbers of edge devices frequently require tight collaboration with application servers situated in a small number of distributed large-scale datacenters, Cloud Internet of Things (CIoT) is a setup that meets this requirement [6, 7]. AI with IoT is also considered a good match because it solves many issues while making IoT devices more intelligent. With the rise of IoT technology, AI has received considerable attention. As a result of this expansion, AI technologies such as decision trees, linear regression, machine learning, support vector machines, and neural networks have been employed in IoT cybersecurity applications to identify dangers and prospective attacks [8] and many other applications [9].We present a thorough overview of previous, current, and future research on ICN-CIoT-AI and associated technologies. For other researchers, we also introduce open research issues. We organize our work into six sections, as shown in the diagram. The first section is an introduction that explains the survey’s purpose and importance. ICN’s properties and deployment settings are discussed in Section2. Section 3 delves more into the background of ICN-CIoT-AI and its contemporary applications. Section 4 contains a thorough work of current researchers in the ICN-CIoT-AI sector, as well as a research synopsis. Section 5 presents open research issues on ICN-CIoT-AI, and we attempt to introduce a few recent advancements in the ICN-CIoT-AI field that can give researchers new horizons to consider. Section 6 draws the conclusions.


## 2. Information-Centric Network

ICN is a novel redesign of the current Internet architecture composed of various features, such as content access by names, and content is universal throughout the network. With the help of the clean slate design of ICN, it has many built-in features, such as location-independent naming, name-based routing, in-network caching, native multicast, and self-secured content. Figure1 displays the comparison between TCP/IP and ICN stacks; the transport and network layers in the TCP/IP stack are replaced by ICN forwarding. ICN can change the current network with different properties and services.Figure 1
Comparison of TCP/IP stack with ICN stack.Many methods and challenges, such as overlay ICN, underlay ICN, clean slate ICN, and hybrid ICN, exist in ICN deployment, as shown in Figure2. The overlay network approach can be used in many ways with ICN (e.g., ICN over user data packet and ICN “island” in an IP “ocean”). This approach is also called tunneling. The recursive layering process, as described in [10], also achieves overlay methodology by using ICN-in-L2-in-IP. When ICN is implemented as an underlay, unlike overlay deployment, this approach does not use tunnels to connect various islands. Protocol conversion gateways or proxies are used to establish connections. The overlay network in ICN is also implemented in where ICN names deal with IPv6 addresses, and other ICN details reside as payloads in IPv6 packet. ICN is introduced as a clean slate approach that is aimed at replacing or renewing the current Internet architecture (i.e., IP architecture). For this purpose, ICN must replace existing routing hardware along with other ancillaries that require ICN-oriented routing and forwarding nodes, such as content-centric routers [11]. The hybrid ICN approach promises the coexistence of IP architecture with ICN implementation by embracing the dual-stack node, which can control the semantic of IP and ICN packets. Given that both protocols are diverse, the dual-stack node uses different options to infer names from IP packets [12]. Recent studies have discussed ICN with 5G network slicing [13]. ICN can provide specific services, such as low latency, supported caching, and mobile caching. Certain studies have also investigated the formation of service slices by means of IP and ICN and have debated the requirement for ICN introduction via a programmable data plane [13].Figure 2
Information-centric network (ICN) as an overlay, underlay, clean slate, hybrid, and slice in 5G.This powerful design can be a good replacement for the current Internet architecture. In this section, ICN architectural components are discussed. ICN mainly comprises two core components that make ICN diverse in the current Internetworking world:(1)
Content access by name(2)
In-network caching


### 2.1. Content Access by Name

Every host over the Internet has a name, and these names are used to access information from the Internet. Information (also called content) is saved with a certain name in a content provider. ICN accesses content in two ways, namely, content discovery and content delivery; content discovery refers to content naming and publisher information and explains how an ICN node entertains it. Content delivery is about content propagation over the ICN network, interest routing toward the best possible content provider, and how ICN routers send content to consumers. In ICN, only the content object representation is the content name, which can be found either on the publisher or anywhere in the ICN network route. ICN content name is the host address replacement. Therefore, the name is a universally unique identification and can temporarily reside in different places, such as an original publisher on an on-path cache. ICN naming can be classified into three distinct classifications:(i)
Universally unique ICN name: names are the only forms of content identification over the ICN network; therefore, names must be unique throughout the ICN communication(ii)
Location-independent: ICN names are location-independent; no matter where the content resides, its name does not change. Content can be replicated and republished anywhere, but its name remains the same. Secure content is a measure of concern in the ICN network(iii)
Object name security: ICN names guarantee security by applying self-certification. Information objects are also self-certified by their names, indicating that names must be signed by their legitimate publishers, and such names must be affixed with the actual content. Moreover, name and data decryption can only be performed by consumers


### 2.2. In-Network Caching

In-network caching is a prominent feature of ICN that makes it different from other network architecture. ICN routers are not only used for forwarding packets but can also cache data during transit, thereby improving hop count, user delay, and other performance parameters. Requested data are end-route to consumers, and ICN routers cache data on all intermediate nodes. ICN routers are similar to IP-based routers. The only difference is their content store (CS) level because ICN routers cache data in their CS for future requests, whereas IP-based routers use a memory buffer that flushes data once transmitted. Network caching, including its application details, is further discussed below. The main advantage of in-network caching is the reduction of round-trip time because content can be found at any intermediate node near a consumer and can be rapidly accessed.


## 2.1. Content Access by Name

Every host over the Internet has a name, and these names are used to access information from the Internet. Information (also called content) is saved with a certain name in a content provider. ICN accesses content in two ways, namely, content discovery and content delivery; content discovery refers to content naming and publisher information and explains how an ICN node entertains it. Content delivery is about content propagation over the ICN network, interest routing toward the best possible content provider, and how ICN routers send content to consumers. In ICN, only the content object representation is the content name, which can be found either on the publisher or anywhere in the ICN network route. ICN content name is the host address replacement. Therefore, the name is a universally unique identification and can temporarily reside in different places, such as an original publisher on an on-path cache. ICN naming can be classified into three distinct classifications:(i)
Universally unique ICN name: names are the only forms of content identification over the ICN network; therefore, names must be unique throughout the ICN communication(ii)
Location-independent: ICN names are location-independent; no matter where the content resides, its name does not change. Content can be replicated and republished anywhere, but its name remains the same. Secure content is a measure of concern in the ICN network(iii)
Object name security: ICN names guarantee security by applying self-certification. Information objects are also self-certified by their names, indicating that names must be signed by their legitimate publishers, and such names must be affixed with the actual content. Moreover, name and data decryption can only be performed by consumers


## 2.2. In-Network Caching

In-network caching is a prominent feature of ICN that makes it different from other network architecture. ICN routers are not only used for forwarding packets but can also cache data during transit, thereby improving hop count, user delay, and other performance parameters. Requested data are end-route to consumers, and ICN routers cache data on all intermediate nodes. ICN routers are similar to IP-based routers. The only difference is their content store (CS) level because ICN routers cache data in their CS for future requests, whereas IP-based routers use a memory buffer that flushes data once transmitted. Network caching, including its application details, is further discussed below. The main advantage of in-network caching is the reduction of round-trip time because content can be found at any intermediate node near a consumer and can be rapidly accessed.


## 3. Background of ICN-CIoT-AI

ICN-AI-CIoT is a combination that addresses contemporary requirements. ICN is a state-of-the-art technology that promises to handle more data than its predecessor. Therefore, IoT is a suitable match for ICN because IoT is also a domain with many devices or nodes, where data are produced unpredictably and need to be pushed somewhere, creating a challenge for the pure pull-based architectures. ICN-IoT [14] was developed with the idea that it can connect billions of devices and manage their information, but today’s Internet is more host-centric than information-centric. Therefore, an architecture is needed with IoT to tackle information with less overhead. ICN provides security, naming, mobility, and in-network caching capability to IoT devices. Figure 3 depicts the ICN-IoT networking with the smart city, smart home, and mobility concept of ICN. ICN-IoT helps to cache crucial data on nodes for speedy data communication and reduces the load over the producer.Figure 3
ICN-IoT networking.Furthermore, because cloud computing, or its extension edge computing, is unable to handle the enormous quantity of contents produced at the network edge, contents generated at the network edge are processed there for effective operations. ICN also plays a key role in this context by allowing natively supported access to data, regardless of where it is kept or generated, as well as overcoming potential data provider disconnections [15]. The key premise for using cloud and ICN technologies to enable immersed human vision is that data created by IoT and users’ devices can be transferred to cloud and ICN platforms dispersed globally and that the outcomes of data elaboration can be conveniently transferred back to users, as shown in Figure 4.Figure 4
ICN-CIoT architecture.Artificial Intelligence is another promising field with ICN with CIoT (ICN-CIoT-AI). The next generation is clearly all about AI. Machines will surpass human intelligence and aid in the growth of humanity, yet this phenomenon will necessitate the efficient use of measures such as speed, time, and space. ICN-CIoT meets the demand by focusing on technological concerns such as information naming, routing and forwarding, caching methods, cloud and edge computing, and handling large and sensitive data. AI technology can assist in breaking the existing ICN development bottleneck. The introduction of intelligent learning and decision-making measures in the data storage and analysis process, in particular, plays an essential role in ensuring the content-centric root of ICN because these processes have a direct effect on the efficiency of content caching and forwarding [16].


## 4. Current Research on ICN-CIoT-AI

We discuss the latest work on ICN-CIoT-AI in this section because of the abundance of research in this area. ICN-CIoT-AI is a combination that opens many horizons such as smart caching for mobile devices with edge computing [17], smart protocol for cluster-based routing for IoT devices using edge computing [18], and ICN sensor network for cognitive IoT using AI [14]. Summaries of techniques and their areas will be discussed in detail.


### 4.1. Related Work

Despite the abundance and diversity of the studies in this area, we attempt a comprehensive review. Tang et al. [17] discussed a smart caching mechanism for mobile devices using edge computing methodology. They introduced a machine learning-based location prediction algorithm and smart caching strategy for predicting user interest. As a result, the user-interest content will be sent from the server to the edge node. For better cache utilization, they proposed an efficient caching replacement approach. In comparison with other existing methods, the experimental findings show that the suggested architecture and technique are effective for caching mobile multimedia content, which can improve the hit ratio and reduce access time. However, the purported algorithm has yet to be tested in a real-world setting. The actual efficacy will then be proven in real-time edge computing.Safitri et al. discussed an AI-based approach for name classification in ICN for IoT [19]. They created a hybrid ICN using machine learning techniques to meet the needs of a practical IoT technology. Before being chosen as the content forwarding method, the algorithms from supervised learning, unsupervised learning, and reinforcement learning were assessed. The numerical results revealed that the extended learning classifier system outperforms the other algorithms when using the reinforcement learning scheme. Nevertheless, alternative machine learning techniques such as logistic regression, naive Bayes, K-nearest neighbors, and decision tree can be used as a benchmark for this experiment.Zhao et al. [20] investigated content-data-friendly ICN architecture in the Internet of Vehicles (IoV) [21] and how they used big data gathering and analysis architecture in ICN to achieve effective route planning for automatic vehicles (AVs). They used the network’s analytical capabilities to gain active cognitive access to traffic data. Simultaneously, game theory was applied to create an incentive system for task distribution and information sharing. Finally, the simulation results demonstrated that the method works, but user privacy and traffic safety, as well as edge information protection, remained issues that need to be addressed.Divya et al. [22] offered an improved ICN-IoT content caching technique for heterogeneous IoT architecture by enabling AI-based collaborative filtering within the edge cloud. This content caching technique based on collaborative filtering would intelligently cache material on edge nodes for cloud database traffic control. The evaluations were carried out to compare the suggested strategy’s performance with that of several benchmark strategies. In comparison to the best-considered strategies, the analytical results show that the proposed strategy performs better in terms of cache hit ratio, content retrieval delay, and average hop count. However, the proposed technique needs to be tested against a variety of named data networking (NDN) designs. To support a scalable network with reduced content retrieval latency, a caching strategy must be designed while considering a vast variety of attributes accessible with the requested content and varying caching capability of all the edge nodes.Campolo et al. [23] presented NDN, an ICN application, and its expansions as an enabler of the transition from centralized AI to distributed edge AI, which meets its unique characteristics well. However, algorithmic design of the NDN components and quantitative assessment of their merits in orchestrating edge AI are required.In terms of the Internet of Vehicular Things, Zhang et al. [24] offered an emergent semantic-based information-centric fog system that enables reliable and intelligent emergency analysis and management. Initially, an effective emergency content dissemination network was created for accumulating and evaluating emergency data. They also offered a semantic-based trustworthy routing strategy for filtering bogus content and harmful entities. However, the emergency scenario in this study was minimal. Deploying the technology and conducting large-scale experiments in a real-life emergency scenario is worthwhile. In Tactile Internet (TI) applications, Ahmed et al. [25] presented an intelligent technique to minimize the stretch in ICN. The many routing paths within the ICN infrastructure are explored and exploited using a Q-learning technique. The Q-learning algorithm is used to solve the problem, which is constructed as a Markov decision process. The simulation findings show that the suggested technique determines the best routing path for delay-sensitive haptic-driven services of 5G-enabled TI, such as augmented reality/virtual reality applications, based on their stretch profile over ICN. The Intelligent Stretch Optimization (ISO) strategy outperforms random routing by 33.69 percent and History Aware Routing Protocol (HARP) by 33.33 percent. Various routing systems should, however, be compared to ISO.Ayadi et al. [26] proposed that deep learning could be used to develop a forwarder for NDN. They surveyed existing AI approaches that apply to networking, as well as a method and algorithm for building a network forwarder that leverages existing machine learning methodologies. Finally, they proposed an application for the forwarder in real-time IoT scenarios. However, because this research uses a grid architecture with 100 NDN nodes, it necessitates a real-time experiment. Pahl et al. [27] discussed a plan to use AI with ICN-IoT by exploiting the caching nature of ICN with data-centric IoT. They also proposed using machine learning to select the appropriate caching technique for each data item and to better predict node behavior. However, criteria such as performance and scalability have yet to be determined through implementation.In the current pandemic situation where the Internet of Medical Things [28] plays a pivotal role to reduce and counter the effects of COVID-19, Khan et al. [29] described a new smart COVID-19 pandemic-controlled eradication mechanism using NDN-IoT (SPICE-IT). SPICE-IT delivers autonomous indoor monitoring with an effective pull-based reporting mechanism that captures violations on local and cloud servers. Every person is examined using an intelligent face mask tracking and temperature measurement mechanism. With an adaptive AI system, the cloud server controls the reaction action from the center. The caching strategy based on long short-term memory lowers cache overflow and overall network congestion.Table1 summarizes the research presented in this section and identifies the technology employed in each study paper in terms of IoT, cloud or edge, and AI/ML. This table also includes information about the various AI/ML technologies discussed in the research articles, aiding researchers in determining the most appropriate technology. Table 2 examines ICN technologies such as ICN for Vehicles (ICN-V), ICN for 5G (ICN-5G), ICN for Medical (ICN-M), NDN, and AI/ML approaches, as well as simulation platforms and results. This table also shows the framework model used in each study, along with simulation and outcomes.Table 1
Summary of research articles.
YearReferenceIoTCloud/edge/fogAI/ML technologies2018[26]YesCloudDeep Neural Network (DNN)2019[17]YesEdgeMachine Learning (ML)2019[20]YesEdgeMultiagent AI systems2019[27]Yes—DNN2020[19]YesCloudML2020[23]YesEdgeArtificial Neural Network (ANN)2021[24]YesCloud/edge/fogNatural Language Processing (NLP) and ML2021[25]YesCloudML2021[29]YesCloudNeural network/ML2021[22]YesCloud/edgeMLTable 2
Detailed AI/ML technique analysis.
ReferenceICN technologiesAI/ML technologiesModel frameworkSimulationResults[26]NDNDeep Neural Network (DNN)A feed-forward two-layer neural network with sigmoid units at the hidden layer and linear units at the output layerndnSIMAccuracy of 99.23%[17]ICNMachine Learning (ML)Frequent pattern- (FP-) growth algorithmSimulation environment, ICN server, edgeNode, and userReduces the time needed to access data and increases the efficiency of data collecting[20]ICN-VMultiagent AI systemsGame theory analysisSimulation mechanism of caching and sharing information in IoV based on evolutionary gamesHelps information sharing in ICN-V[27]ICNDNNDeep learning as a black box technology and least recently used (LRF) or least frequently used (LFU)No simulation—[19]ICNMLMultilayer perceptron (MLP) for supervised learning, deep belief network- restricted Boltzmann machines (DBN-RBM) for unsupervised learning, and extended learning classifier system under reinforcement learning (RL)Calculate root mean square error (RMSE) using MATLABXCS algorithm under RL methods outperforms the others[23]NDNArtificial Neural Network (ANN)Caching and replacement decisions by using model/parameter/inferenceNo simulation—[24]ICN-VNatural Language Processing (NLP) and MLSemantic-based analysisTestbed and EdgeCloudSimPerforms commendably in emergency situations[25]ICN-5GMLRL, Q-learning/Markov decision processMATLABBased on their stretch profile over ICN, the technique finds the best routing path for delay-sensitive 5G-enabled TI. ISO strategy outperforms random routing by 33.69% and HARP by 33.33%[29]ICN-MNeural network/MLImage classification/convolutional networkndnSim, Keras, and tensor for deep learningSmart mechanism to monitor and counter COVID-19 pandemic spread in close indoor environments[22]ICNMLK-means clusteringIcarus simulator (ICN caching simulator)In comparison with the best-considered schemes, the proposed technique demonstrates a 15% increase in cache hit ratio, a 12% reduction in content retrieval delay, and a 28% reduction in average hop count


## 4.1. Related Work

Despite the abundance and diversity of the studies in this area, we attempt a comprehensive review. Tang et al. [17] discussed a smart caching mechanism for mobile devices using edge computing methodology. They introduced a machine learning-based location prediction algorithm and smart caching strategy for predicting user interest. As a result, the user-interest content will be sent from the server to the edge node. For better cache utilization, they proposed an efficient caching replacement approach. In comparison with other existing methods, the experimental findings show that the suggested architecture and technique are effective for caching mobile multimedia content, which can improve the hit ratio and reduce access time. However, the purported algorithm has yet to be tested in a real-world setting. The actual efficacy will then be proven in real-time edge computing.Safitri et al. discussed an AI-based approach for name classification in ICN for IoT [19]. They created a hybrid ICN using machine learning techniques to meet the needs of a practical IoT technology. Before being chosen as the content forwarding method, the algorithms from supervised learning, unsupervised learning, and reinforcement learning were assessed. The numerical results revealed that the extended learning classifier system outperforms the other algorithms when using the reinforcement learning scheme. Nevertheless, alternative machine learning techniques such as logistic regression, naive Bayes, K-nearest neighbors, and decision tree can be used as a benchmark for this experiment.Zhao et al. [20] investigated content-data-friendly ICN architecture in the Internet of Vehicles (IoV) [21] and how they used big data gathering and analysis architecture in ICN to achieve effective route planning for automatic vehicles (AVs). They used the network’s analytical capabilities to gain active cognitive access to traffic data. Simultaneously, game theory was applied to create an incentive system for task distribution and information sharing. Finally, the simulation results demonstrated that the method works, but user privacy and traffic safety, as well as edge information protection, remained issues that need to be addressed.Divya et al. [22] offered an improved ICN-IoT content caching technique for heterogeneous IoT architecture by enabling AI-based collaborative filtering within the edge cloud. This content caching technique based on collaborative filtering would intelligently cache material on edge nodes for cloud database traffic control. The evaluations were carried out to compare the suggested strategy’s performance with that of several benchmark strategies. In comparison to the best-considered strategies, the analytical results show that the proposed strategy performs better in terms of cache hit ratio, content retrieval delay, and average hop count. However, the proposed technique needs to be tested against a variety of named data networking (NDN) designs. To support a scalable network with reduced content retrieval latency, a caching strategy must be designed while considering a vast variety of attributes accessible with the requested content and varying caching capability of all the edge nodes.Campolo et al. [23] presented NDN, an ICN application, and its expansions as an enabler of the transition from centralized AI to distributed edge AI, which meets its unique characteristics well. However, algorithmic design of the NDN components and quantitative assessment of their merits in orchestrating edge AI are required.In terms of the Internet of Vehicular Things, Zhang et al. [24] offered an emergent semantic-based information-centric fog system that enables reliable and intelligent emergency analysis and management. Initially, an effective emergency content dissemination network was created for accumulating and evaluating emergency data. They also offered a semantic-based trustworthy routing strategy for filtering bogus content and harmful entities. However, the emergency scenario in this study was minimal. Deploying the technology and conducting large-scale experiments in a real-life emergency scenario is worthwhile. In Tactile Internet (TI) applications, Ahmed et al. [25] presented an intelligent technique to minimize the stretch in ICN. The many routing paths within the ICN infrastructure are explored and exploited using a Q-learning technique. The Q-learning algorithm is used to solve the problem, which is constructed as a Markov decision process. The simulation findings show that the suggested technique determines the best routing path for delay-sensitive haptic-driven services of 5G-enabled TI, such as augmented reality/virtual reality applications, based on their stretch profile over ICN. The Intelligent Stretch Optimization (ISO) strategy outperforms random routing by 33.69 percent and History Aware Routing Protocol (HARP) by 33.33 percent. Various routing systems should, however, be compared to ISO.Ayadi et al. [26] proposed that deep learning could be used to develop a forwarder for NDN. They surveyed existing AI approaches that apply to networking, as well as a method and algorithm for building a network forwarder that leverages existing machine learning methodologies. Finally, they proposed an application for the forwarder in real-time IoT scenarios. However, because this research uses a grid architecture with 100 NDN nodes, it necessitates a real-time experiment. Pahl et al. [27] discussed a plan to use AI with ICN-IoT by exploiting the caching nature of ICN with data-centric IoT. They also proposed using machine learning to select the appropriate caching technique for each data item and to better predict node behavior. However, criteria such as performance and scalability have yet to be determined through implementation.In the current pandemic situation where the Internet of Medical Things [28] plays a pivotal role to reduce and counter the effects of COVID-19, Khan et al. [29] described a new smart COVID-19 pandemic-controlled eradication mechanism using NDN-IoT (SPICE-IT). SPICE-IT delivers autonomous indoor monitoring with an effective pull-based reporting mechanism that captures violations on local and cloud servers. Every person is examined using an intelligent face mask tracking and temperature measurement mechanism. With an adaptive AI system, the cloud server controls the reaction action from the center. The caching strategy based on long short-term memory lowers cache overflow and overall network congestion.Table1 summarizes the research presented in this section and identifies the technology employed in each study paper in terms of IoT, cloud or edge, and AI/ML. This table also includes information about the various AI/ML technologies discussed in the research articles, aiding researchers in determining the most appropriate technology. Table 2 examines ICN technologies such as ICN for Vehicles (ICN-V), ICN for 5G (ICN-5G), ICN for Medical (ICN-M), NDN, and AI/ML approaches, as well as simulation platforms and results. This table also shows the framework model used in each study, along with simulation and outcomes.Table 1
Summary of research articles.
YearReferenceIoTCloud/edge/fogAI/ML technologies2018[26]YesCloudDeep Neural Network (DNN)2019[17]YesEdgeMachine Learning (ML)2019[20]YesEdgeMultiagent AI systems2019[27]Yes—DNN2020[19]YesCloudML2020[23]YesEdgeArtificial Neural Network (ANN)2021[24]YesCloud/edge/fogNatural Language Processing (NLP) and ML2021[25]YesCloudML2021[29]YesCloudNeural network/ML2021[22]YesCloud/edgeMLTable 2
Detailed AI/ML technique analysis.
ReferenceICN technologiesAI/ML technologiesModel frameworkSimulationResults[26]NDNDeep Neural Network (DNN)A feed-forward two-layer neural network with sigmoid units at the hidden layer and linear units at the output layerndnSIMAccuracy of 99.23%[17]ICNMachine Learning (ML)Frequent pattern- (FP-) growth algorithmSimulation environment, ICN server, edgeNode, and userReduces the time needed to access data and increases the efficiency of data collecting[20]ICN-VMultiagent AI systemsGame theory analysisSimulation mechanism of caching and sharing information in IoV based on evolutionary gamesHelps information sharing in ICN-V[27]ICNDNNDeep learning as a black box technology and least recently used (LRF) or least frequently used (LFU)No simulation—[19]ICNMLMultilayer perceptron (MLP) for supervised learning, deep belief network- restricted Boltzmann machines (DBN-RBM) for unsupervised learning, and extended learning classifier system under reinforcement learning (RL)Calculate root mean square error (RMSE) using MATLABXCS algorithm under RL methods outperforms the others[23]NDNArtificial Neural Network (ANN)Caching and replacement decisions by using model/parameter/inferenceNo simulation—[24]ICN-VNatural Language Processing (NLP) and MLSemantic-based analysisTestbed and EdgeCloudSimPerforms commendably in emergency situations[25]ICN-5GMLRL, Q-learning/Markov decision processMATLABBased on their stretch profile over ICN, the technique finds the best routing path for delay-sensitive 5G-enabled TI. ISO strategy outperforms random routing by 33.69% and HARP by 33.33%[29]ICN-MNeural network/MLImage classification/convolutional networkndnSim, Keras, and tensor for deep learningSmart mechanism to monitor and counter COVID-19 pandemic spread in close indoor environments[22]ICNMLK-means clusteringIcarus simulator (ICN caching simulator)In comparison with the best-considered schemes, the proposed technique demonstrates a 15% increase in cache hit ratio, a 12% reduction in content retrieval delay, and a 28% reduction in average hop count


## 5. Open Issues and Challenges

ICN-CIoT-AI is a fast emerging and evolving field that attracts researchers. Many open issues and challenges can be studied, such as security, in-network caching, heterogeneous networking, mobility, and automation [30–33]. In this paper, ICN-CIoT-AI open issues and challenges are categorized in three distinctive sections: security, performance, and in-network caching. Table 3 summarizes the future study fields and technologies addressed in this article, as well as a review of open issues for researchers. Figure 5 depicts an overview of open issues in relation to the technologies addressed in this paper.Table 3
Summary of future research areas in ICN-CIoT-AI.
ReferencesTechnologyFuture research areasSecurity[34]ICN-CIoT-AIIntrusion detection, privacy preservation, threat intelligence, and hunting[35]ICN-V-CIoTBlockchain-based security framework for ICN-IoT network architectures, NDN-based unmanned aerial vehicle ad hoc networks, and smart building networks[36]ICN-IoT-AISparse coding to further protect the user’s privacy in the future without compromising data utility[37]ICN-V-CIoTImprovements to the algorithm are required to include incentive-based participation while ensuring security and privacy with minimal cryptographic overhead[38]ICN-V IoT-AICaching policies and forwarding techniques, deployment of increasingly complex attacker scenarios[39]ICN-IoTThe proposed mechanism will need to be tested in more realistic topologiesPerformance[18]ICNWSN-CIoT-AIML techniques can be used to improve the performance of the ICNWSN models[40]ICN-IoTScalability of content-oriented ICN–IoT networks, naming, security, and routing all improve the performance[41]NDN-V-CIoTContext-aware data ownership, which increases the performance of NDN-V in a cloud or mobile environmentIn-network caching[42]ICN-5G-CIoTIncreasing/decreasing cache size, efficient cache replacement policies, and wireless channel parameters affect latency and capacity versus latency[43]ICN-CIoT-AIDeep learning-based deduplication on index tables, as well as a CBAD platform, could be utilized to offer secure and energy-efficient content[44]ICN-V-IoTV2V communications, data relating to infotainment for passengers inside automobiles[45]NDN-V-IoTThe allocation of three categories of data in the cache storage that has not been thoroughly explored, such as the emergency message and the other two messages, can only be received from the original producer node, reducing acquisition efficiency significantly[46]ICN-IoTThis study is open to including support for IoT node mobility using NDN networkingFigure 5
Summary of open research issues.


### 5.1. Security

Many studies have already investigated security, but in this paper, we first class issues related with ICN-CIoT-AI specifically. Moustafa [34] proposed domains that might be explored in the future to evaluate a variety of AI-based cybersecurity applications, including intrusion detection, privacy preservation, threat intelligence, and hunting. Lei et al. [35] identified a future endeavor centered on expanding a blockchain-based security framework for a range of ICN-IoT network architectures, such as NDN-based unmanned aerial vehicle ad hoc networks and smart building networks. In ICN-IoT-AI privacy, Cao et al. [36] expressed interest in extending sparse coding to further protect the user’s privacy in the future without compromising data utility. Nkenyerey et al. [37] indicated their intention to develop their method to include incentive-based participation while ensuring security and privacy with little cryptographic cost in the context of ICN-V and cloud.In ICN-V, Magaia and Sheng [38] provided a unique ML-based approach for ICN-V applications such as content dissemination. They also outlined the future of their research, including caching policies and forwarding techniques, as well as the deployment of increasingly complex attacker scenarios. Zhi et al. [39] suggested a Reputation Value-based Early Detection (RVED) technique to prevent the unfavorable network consequences induced by a consumer-provider collusive attack in the setting of ICN-IoT. To be validated, this mechanism will need to be tested in more realistic topologies and built into an actual system in the future.


### 5.2. Performance

Performance in ICN-CIoT-AI is another crucial factor that is being addressed by researchers. In information-centric wireless sensor networks (ICNWSN), Vaiyapuri et al. [18] introduced a novel hybrid optimization technique for cluster-based routing protocol for IoT-based mobile edge computing. In terms of network lifetime and energy efficiency, this technique outperformed the alternatives. ML techniques can be used to improve the performance of the ICNWSN models in the future. Nour et al. [40] described the emergence, evolution, and state-of-the-art mobility method in their study about the merger of ICN and IoT. They also identified some concerns about the scalability of content-oriented ICN–IoT networks, including naming, routing, and security. To ensure superior network performance, the mobility management of such a scaled system is extremely demanding. Hussain et al. [41] gave a survey report on NDN for Vehicle (NDN-V) with cloud and also discussed context-aware data ownership, which has a direct effect on performance in NDN-V in a cloud or mobile environment.


### 5.3. In-Network Caching

Edge caching is critical for lowering latency and increasing spectral and energy efficiency. In the context of caching in ICN with edge for 5G and IoT, Rehmat et al. [42] disclosed that some research issues remain unsolved and require further attention. Researchers should look into how increasing/decreasing cache size, efficient cache replacement policies, and wireless channel parameters affect latency. Overall, while designing effective cache methods, researchers are advised to investigate various trade-offs such as capacity versus latency and other factors. In this regard, Sakthivanitha and Saradha [43] discussed the issues related to ICN-based IoT environment and concluded that deep learning-based deduplication on index tables, as well as a cloud-based application delivery (CBAD) platform, could be utilized to offer secure and energy-efficient content. The Left-Right-Front (LRF) caching system was described by Din et al. [44]. The suggested ICN-based LRF caching approach was created specifically for ICN-V, and it outperforms existing popular strategies. Din et al. suggested that their work might be expanded to include V2V communications. The method might potentially be tailored to work for data relating to infotainment for passengers inside automobiles. In NDN-V, Chen et al. [45] presented the intelligent caching strategy. The simulation results showed that the cache strategy proposed in this study may successfully reduce data redundancy in the network’s cache space while also maximizing the effective cache space, as evidenced by the average hit ratio, average hop count, and average cache replacement timings. However, several issues remain unsolved, such as the allocation of three categories of data in the cache area, which has not been thoroughly investigated. The most important message should be the emergency safety message. However, if all of the cache capacity is dedicated to emergency safety messages, the other two types of messages can only be retrieved from the original producer node, reducing acquisition efficiency significantly.Anamalamudi et al. [46] provided a caching approach in ICN-IoT networks, stating that this study provides a cooperative caching strategy for CS and retrieval in “machine-to-machine” ICN-IoT networks. To reduce IoT node energy consumption and data retrieval delay, the suggested approach combined NDN networking with IoT data lifespan (content value) and spatial data storage. The cache hit ratio, node energy consumption, and packet retrieval latency of the proposed cooperative caching system were compared with those of the conventional standalone caching mechanism. Yet, this study was open to including support for IoT node mobility using NDN networking.


## 5.1. Security

Many studies have already investigated security, but in this paper, we first class issues related with ICN-CIoT-AI specifically. Moustafa [34] proposed domains that might be explored in the future to evaluate a variety of AI-based cybersecurity applications, including intrusion detection, privacy preservation, threat intelligence, and hunting. Lei et al. [35] identified a future endeavor centered on expanding a blockchain-based security framework for a range of ICN-IoT network architectures, such as NDN-based unmanned aerial vehicle ad hoc networks and smart building networks. In ICN-IoT-AI privacy, Cao et al. [36] expressed interest in extending sparse coding to further protect the user’s privacy in the future without compromising data utility. Nkenyerey et al. [37] indicated their intention to develop their method to include incentive-based participation while ensuring security and privacy with little cryptographic cost in the context of ICN-V and cloud.In ICN-V, Magaia and Sheng [38] provided a unique ML-based approach for ICN-V applications such as content dissemination. They also outlined the future of their research, including caching policies and forwarding techniques, as well as the deployment of increasingly complex attacker scenarios. Zhi et al. [39] suggested a Reputation Value-based Early Detection (RVED) technique to prevent the unfavorable network consequences induced by a consumer-provider collusive attack in the setting of ICN-IoT. To be validated, this mechanism will need to be tested in more realistic topologies and built into an actual system in the future.


## 5.2. Performance

Performance in ICN-CIoT-AI is another crucial factor that is being addressed by researchers. In information-centric wireless sensor networks (ICNWSN), Vaiyapuri et al. [18] introduced a novel hybrid optimization technique for cluster-based routing protocol for IoT-based mobile edge computing. In terms of network lifetime and energy efficiency, this technique outperformed the alternatives. ML techniques can be used to improve the performance of the ICNWSN models in the future. Nour et al. [40] described the emergence, evolution, and state-of-the-art mobility method in their study about the merger of ICN and IoT. They also identified some concerns about the scalability of content-oriented ICN–IoT networks, including naming, routing, and security. To ensure superior network performance, the mobility management of such a scaled system is extremely demanding. Hussain et al. [41] gave a survey report on NDN for Vehicle (NDN-V) with cloud and also discussed context-aware data ownership, which has a direct effect on performance in NDN-V in a cloud or mobile environment.


## 5.3. In-Network Caching

Edge caching is critical for lowering latency and increasing spectral and energy efficiency. In the context of caching in ICN with edge for 5G and IoT, Rehmat et al. [42] disclosed that some research issues remain unsolved and require further attention. Researchers should look into how increasing/decreasing cache size, efficient cache replacement policies, and wireless channel parameters affect latency. Overall, while designing effective cache methods, researchers are advised to investigate various trade-offs such as capacity versus latency and other factors. In this regard, Sakthivanitha and Saradha [43] discussed the issues related to ICN-based IoT environment and concluded that deep learning-based deduplication on index tables, as well as a cloud-based application delivery (CBAD) platform, could be utilized to offer secure and energy-efficient content. The Left-Right-Front (LRF) caching system was described by Din et al. [44]. The suggested ICN-based LRF caching approach was created specifically for ICN-V, and it outperforms existing popular strategies. Din et al. suggested that their work might be expanded to include V2V communications. The method might potentially be tailored to work for data relating to infotainment for passengers inside automobiles. In NDN-V, Chen et al. [45] presented the intelligent caching strategy. The simulation results showed that the cache strategy proposed in this study may successfully reduce data redundancy in the network’s cache space while also maximizing the effective cache space, as evidenced by the average hit ratio, average hop count, and average cache replacement timings. However, several issues remain unsolved, such as the allocation of three categories of data in the cache area, which has not been thoroughly investigated. The most important message should be the emergency safety message. However, if all of the cache capacity is dedicated to emergency safety messages, the other two types of messages can only be retrieved from the original producer node, reducing acquisition efficiency significantly.Anamalamudi et al. [46] provided a caching approach in ICN-IoT networks, stating that this study provides a cooperative caching strategy for CS and retrieval in “machine-to-machine” ICN-IoT networks. To reduce IoT node energy consumption and data retrieval delay, the suggested approach combined NDN networking with IoT data lifespan (content value) and spatial data storage. The cache hit ratio, node energy consumption, and packet retrieval latency of the proposed cooperative caching system were compared with those of the conventional standalone caching mechanism. Yet, this study was open to including support for IoT node mobility using NDN networking.


## 6. Conclusion

ICN is an ongoing approach, and many studies call for further investigations to prove that ICN is the best replacement for the current Internet. This article attempts to fully scrutinize existing technologies related to ICN-CIoT-AI, and we discuss research challenges that may prove valuable in comprehending this domain. Future studies in this area should focus on open areas in different ICN-CIoT-AI schemes so that readers can draw ideas to create a de facto architecture for ICN-CIoT-AI that can address the utmost limitations in available approaches.



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*Source: 7818712-2022-06-20.xml*