### 5.3.2 Experiment 2: Detection and Classification of Denial of Service Attacks in IoT Networks

According to Mittal et al., [131], Wireless Sensor Networks (WSNs) are extremely susceptible to Denial-of-Service (DoS) threats, especially in hostile environments where sensor nodes can be physically accessed and controlled. DoS attacks, as described by Bhatt et al. [132], can severely impact internet bandwidth and cause traditional connections to terminate by overwhelming the network with large amounts of unnecessary data. WSN jamming threats have been categorized into different categories of DoS threats, such as Blackhole, Grayhole, Flooding, and Scheduling [133].

WSNs and IoT systems are susceptible in almost every layer, which renders them an attractive target for a variety of DoS assaults. Data-driven methods based on DL/ML-based approaches have been used to develop efficient defensive tactics and remedies for security breaches, notably DoS assaults.

Based on our proposed approach, we propose a solution for the detection and classification of DoS attacks [134]. The following points highlight the primary findings and contributions of our experiment:

* Put forth a deep TL technique to detect and categorize DoS assaults in WSNs based on microservices;

* Transform tabular data into images using a visual method;

* Train different pre-trained CNN architectures to detect and classify DoS attacks;

* Employ the WSN-DS dataset for learning and experiments.

* Use the ensemble learning method to provide accurate decisions;

* Compare findings with related works.

#### 5.3.2.1 Dataset

This study uses the WSN-DS dataset [135] to evaluate the proposed solution. WSN-DS was gathered using the Low Energy Aware Cluster Hierarchy (LEACH) protocol
[136], which is a well-known hierarchical routing protocol in WSNs. This dataset is very huge and includes 19 attributes and four DoS attack categories, namely Blackhole, Grayhole, Flooding, Scheduling, and also normal (benign) samples.

### 5.3.2.2 Data Preprocessing

#### 1. Data Integration:

The labels for different sorts of assaults must be transformed to numerical values to ensure that they do not impact the efficiency of the CNNs utilized in the trials. The WSN-DS dataset comprises Blackhole, Flooding, Grayhole, Normal, and Scheduling assaults, which are denoted in the results of the investigations as 0, 1, 2, 3, and 4.

#### 2. Data Normalization:

The id and time features are removed from the dataset. Using Equation 5.5, each of the values of the other sixteen attributes is normalized around 0.0 and 1.0.

$$A' = \frac{(A - A_{min})}{(A_{max} - A_{min})} \qquad (5.5)$$

where A represents the unprocessed attribute value, while Amax and Amin refer to the maximum and minimum feature values, respectively.

#### 3. Data Transformation:

Following dataset preparation, The Image Generator for Tabular Data (IGTD) technique is applied to transform the WSN-DS data towards image representations [137]. Figure 5.6 shows several examples of the created images.

<figure><img src="image_23.png" /><figcaption>FIGURE 5.6: Visual representations of the dataset using IGTD technique.</figcaption></figure>
### 5.3.2.3 Models Building

The objective of this step is to tackle the issue of detecting and categorizing new DoS attacks using existing knowledge. Instead of creating and teaching DL models from the beginning, which is time-consuming and requires a significant amount of computational resources and data, the use of already learned CNNs can enhance and expedite the learning process by reusing their layers' weights. To detect and classify different DoS threats, ResNet18, DenseNet161, VGG16, SqueezeNet, and EfficientNetB3 were employed. The five CNN architectures were learned over 25 epochs with Adam optimizer, cross-entropy loss function, and SoftMax activation function. The hyper-parameters employed for the configuration of model training are presented in Table 5.4.

<table><caption>TABLE 5.4: Specified hyper-parameters for optimal models performance</caption><thead><tr><td>Hyper-parameters</td><td>Values</td></tr></thead><tbody><tr><td>Batch size</td><td>64</td></tr><tr><td>Epochs</td><td>25</td></tr><tr><td>Images size</td><td>150, 150</td></tr><tr><td>Optimizer</td><td>Adam</td></tr><tr><td>Loss function</td><td>Cross-entropy</td></tr></tbody></table>

### 5.3.2.4 Interpretation

The CNN models were learned using the preprocessed data, and their effectiveness was evaluated using the evaluation metrics and training time measures. Test images were employed to assess the overall performance of the models, and Table 5.5 shows that the DenseNet161 CNN achieved high performance and reached an accuracy of 99.995%. The VGG16 was the model with the lowest performance. Overall, the learned models performed well and accurately, with no notable variances in their outcomes. ResNet18, SqueezeNet, and EfficientNetB3 all functioned similarly, having an accuracy of around 99.9%.

Due to the impressive performance of achieving 99.9%, we conducted a thorough examination of overfitting to ensure the reliability of the results. We assessed the loss obtained from the learned models, which served as an indicator of the model's ability to generalize beyond the training data.
The following stage was to use ensemble learning by integrating the findings of CNNs utilizing the hard voting approach. The performance provided by the ensemble classifier was evaluated using the test set of data, and Table 5.5 compares the outcomes of the TL-based DL models. The proposed ensemble classifier produced higher classification results and outperformed individual CNNs with 100% precision, recall, F1-score, and accuracy. The TL-based ensemble classifier performed admirably in identifying and classifying DoS assaults.

These experimental findings emphasize the importance to use TL as well as ensemble learning in detecting and classifying DoS assaults correctly and robustly.

<table><caption>TABLE 5.5: Evaluation results of TL-based classifiers on the WSN-DS dataset.</caption><thead><tr><th>Model</th><th>Class</th><th>Precision</th><th>Recall</th><th>F1-score</th><th>Accuracy</th><th>Loss</th></tr></thead><tbody><tr><td rowspan="5">ResNet18</td><td>Blackhole</td><td>99.90</td><td>99.95</td><td>99.92</td><td rowspan="5">99.983</td><td rowspan="5">0.0021</td></tr><tr><td>Flooding</td><td>98.95</td><td>100</td><td>100</td></tr><tr><td>Grayhole</td><td>100</td><td>99.96</td><td>99.98</td></tr><tr><td>Normal</td><td>100</td><td>99.95</td><td>99.97</td></tr><tr><td>Scheduling</td><td>99.77</td><td>99.84</td><td>99.81</td></tr><tr><td rowspan="5">DenseNet161</td><td>Blackhole</td><td>100</td><td>100</td><td>100</td><td rowspan="5">99.995</td><td rowspan="5">0.0035</td></tr><tr><td>Flooding</td><td>100</td><td>99.84</td><td>99.92</td></tr><tr><td>Grayhole</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Normal</td><td>99.99</td><td>100</td><td>99.99</td></tr><tr><td>Scheduling</td><td>100</td><td>100</td><td>100</td></tr><tr><td rowspan="5">VGG16</td><td>Blackhole</td><td>99.90</td><td>99.95</td><td>99.92</td><td rowspan="5">99.951</td><td rowspan="5">0.0016</td></tr><tr><td>Flooding</td><td>98.95</td><td>100</td><td>100</td></tr><tr><td>Grayhole</td><td>100</td><td>99.96</td><td>99.98</td></tr><tr><td>Normal</td><td>100</td><td>99.95</td><td>99.97</td></tr><tr><td>Scheduling</td><td>99.77</td><td>99.84</td><td>99.81</td></tr><tr><td rowspan="5">SqueezeNet</td><td>Blackhole</td><td>99.95</td><td>100</td><td>99.97</td><td rowspan="5">99.959</td><td rowspan="5">0.0013</td></tr><tr><td>Flooding</td><td>99.39</td><td>99.54</td><td>100</td></tr><tr><td>Grayhole</td><td>99.93</td><td>100</td><td>99.65</td></tr><tr><td>Normal</td><td>99.98</td><td>99.97</td><td>99.98</td></tr><tr><td>Scheduling</td><td>100</td><td>99.77</td><td>99.88</td></tr><tr><td rowspan="5">EfficientNetB3</td><td>Blackhole</td><td>100</td><td>100</td><td>100</td><td rowspan="5">99.991</td><td rowspan="5">0.0021</td></tr><tr><td>Flooding</td><td>100</td><td>99.69</td><td>99.84</td></tr><tr><td>Grayhole</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Normal</td><td>99.98</td><td>100</td><td>99.99</td></tr><tr><td>Scheduling</td><td>100</td><td>100</td><td>100</td></tr><tr><td rowspan="5">Proposed Approach</td><td>Blackhole</td><td>100</td><td>100</td><td>100</td><td rowspan="5">100</td><td rowspan="5">0</td></tr><tr><td>Flooding</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Grayhole</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Normal</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Scheduling</td><td>100</td><td>100</td><td>100</td></tr></tbody></table>
<figure><img src="image_24.png" /><figcaption>FIGURE 5.7: Comparison of the proposed approach's performance with existing studies in the literature.</figcaption></figure>

### 5.3.2.5 Comparison with Similar Studies

The suggested solution for detecting DoS attacks in WSNs using DL, TL, and ensemble learning techniques was compared to previously published studies using the same data. The results demonstrated that this solution obtained a high recall percentage compared to the other reviewed studies, which are based on traditional neural networks. By transforming tabular data into images and using pre-trained CNN architectures, the approach efficiently dealt with the dataset's unbalanced distribution among classes. The TL and ensemble learning paradigms had a considerable influence on the effectiveness of the implemented models in coping with the WSN-DS dataset's extreme unbalance and achieving excellent recall, F1-score, and accuracy values across each DoS class.

## 5.4 Case Study 2: Healthcare

The fast rise of IoT and big data analytics has transformed the medical sector, opening the door for the creation of intelligent healthcare systems. By utilizing cutting-edge technology like IoT and big data analytics, healthcare providers can
now offer personalized, real-time care to patients, leading to improved health out-
comes. In this view, we use the proposed approach to help in a smart and effective
disease prediction.

5.4.1 Diagnosis of Pneumonia Using Chest X-Ray Imagery

The smart healthcare sector has greatly benefited from the rapid advancements
in IoT and big data analytics, leading to improved patient care and well-being.
Various IoT applications have been developed using data analytics techniques for
various medical services such as illness forecasts, nutrition evaluation, and elderly
care.

For pneumonia detection, we utilized TL with a fine-tuning method on 5 previ-
ously learned CNN architectures (VGG16, InceptionV3, RestNet50, Xception, and
DenseNet201) trained through the FL. Our solution also involves combining the
decisions extracted from these models using the Dempster-Shafer Theory (DST)
to achieve high-performance image classification results. The last categorization
choice is reached by combining the outcomes of the trained models used. Figure 5.8
illustrates the different stages of the pneumonia detection use case.

5.4.1.1 Dataset

The study conducted experiments on the Pneumonia Chest X-Ray dataset, which
was gathered by the Guangzhou Women and Children’s Medical Center in China
and is accessible on Kaggle [138]. The data set includes 5855 images for training,
validation, and testing groups, containing 1591 normal images and 4273 infected
ones.

Although the dataset itself may not be directly generated from IoT devices, its
application in the context of healthcare and medical monitoring aligns with the
broader theme of IoT.
<figure><img src="image_25.png" /><figcaption>FIGURE 5.8: Pneumonia detection scenario following the proposed approach.</figcaption></figure>
5.4.1.2 Data Preprocessing

To enhance the performance of classifiers, we utilized seven different techniques to augment the dataset. These included resizing the images to 224x224 and employing methods like rescaling, horizontal flipping, random rotation, width and height shift, and adjusting zoom and brightness levels.

5.4.1.3 Models Building

The proposed approach was implemented using the TensorFlow Federated framework on 5 virtual IoT devices in a federated setting. Five pre-trained CNN architectures were utilized with TL for efficient pneumonia detection, including VGG16, Xception, InceptionV3, ResNet50, and DenseNet201. Models' aggregation was performed over 15 communication rounds, with each CNN trained for 20 epochs using local data. The Adam optimizer with a learning rate of 1e-4 and the cross-entropy loss function were used for model configuration. Input images were resized to 224x224 pixels and normalized, and a batch size of 32 was used during training. To facilitate the self-learning process, the dataset was divided into training and test data sets across 5 different clients.

5.4.1.4 Interpretation Stage

After training the five DL classifiers, their performance was evaluated using the testing set of chest X-ray images. The classification results of each classifier are shown in Table 5.7. The DenseNet201 model achieved the best performance. The performance difference between the models is not significant, indicating relatively balanced performance.

Following the training phase, Dempster's theory was applied to combine the models'
outputs. The performance outcomes of the generated classifiers are presented in
Table 5.6. The combined classifier outperforms the individual models in terms of
accuracy with 98.1%.
<table><caption>TABLE 5.6: Performance results of the learned DL models and the proposed classifier.</caption><thead><tr><th>Model// Metric</th><th>VGG16</th><th>Xception</th><th>InceptionV3</th><th>ResNet50</th><th>DenseNet201</th><th>Proposed classifier</th></tr></thead><tbody><tr><td>Accuracy</td><td>94.8</td><td>95.6</td><td>96.2</td><td>95.1</td><td>96.4</td><td>98.1</td></tr><tr><td>Precision</td><td>94.2</td><td>95.4</td><td>96.4</td><td>94.6</td><td>96.8</td><td>97.9</td></tr><tr><td>Recall</td><td>95.0</td><td>96.2</td><td>96.9</td><td>95.3</td><td>96.5</td><td>98.3</td></tr><tr><td>F1-score</td><td>94.6</td><td>95.8</td><td>96.6</td><td>94.9</td><td>96.6</td><td>98.1</td></tr></tbody></table>

### 5.4.1.5 Experimental Evaluation

#### Microservices Performance Assessment

Moreover, the performance of the proposed approach was assessed by evaluating the execution time of microservices based on their functionality. The end-to-end response time of the pneumonia detection function *f* is defined as the sum of the time spent in data pre-processing *T*<sub>Pre</sub>, Interpretation *T*<sub>Inter</sub>, and results fusion *T*<sub>Fus</sub> stages, as shown in Equation 5.6.

$$T_f = T_{Pre} + T_{Inter} + T_{Fus} \qquad (5.6)$$

Figure 5.9 showcases the execution time of each microservice in the pneumonia detection process, encompassing data preparation, interpretation, and results fusion. The data preprocessing microservice took 198 ms, while the interpretation microservice took 564 ms. On the other hand, the results' fusion microservice enhanced performance with an execution time of only 198 ms. As a result, the total time for the pneumonia detection process was calculated to be approximately 960 ms.

#### Comparison

To validate the proposed approach, we compared our previously published study, which focused on the same use case scenario and utilized the same dataset but adopted a centralized learning setting with monolithic architectures [65]. The performance results, as depicted in Table 5.7, clearly demonstrate that our approach outperforms the previous study in terms of achieving the highest performance results. This validates the effectiveness of our approach that utilizes decentralized DL models in an FL architecture.
<figure><img src="image_26.png" /><figcaption>FIGURE 5.9: Microservices execution time for the pneumonia detection process.</figcaption></figure>

<table><caption>TABLE 5.7: Comparison of the proposed approach results with our previously published study</caption><thead><tr><th>Model</th><th>Type of learning</th><th>Architecture</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td>[65]</td><td>Centralized</td><td>Monolithic</td><td>97.5</td><td>97.5</td><td>98</td><td>97.8</td></tr><tr><td>Proposed solution</td><td>FL</td><td>Microservices</td><td>98.1</td><td>97.9</td><td>98.3</td><td>98.1</td></tr></tbody></table>

## Discussion

To ensure accurate pneumonia diagnosis, it is essential to identify all relevant features present in the chest X-ray images. To enhance model generalization, ensemble learning was employed to combine the predictions of multiple TL models to make more accurate and robust predictions.

Privacy concerns arise when dealing with sensitive medical data. For this, FL was used to address these concerns by enabling the training of models on distributed data while keeping the data securely on their local devices. This allowed for collaborative model training across multiple institutions without sharing the raw data, preserving privacy and confidentiality.

In addition to privacy considerations, achieving high scalability and response time is crucial in healthcare. Microservices architecture was leveraged to address these requirements by breaking the data analytics function into smaller, specialized microservices that can be deployed and scaled independently. This allowed for efficient resource utilization and improved scalability. Moreover, microservices architecture enabled faster response times as each microservice can be optimized for performance and response time, leading to quicker results for end-users.
A robust and efficient pneumonia diagnosis solution was achieved by combin-
ing ensemble learning for model generalization, FL for privacy preservation, and
microservices architecture for scalability and response time optimization. This ap-
proach ensures accurate predictions, protects patient privacy, and enables efficient
and scalable data analytics in the healthcare environment.

5.5 Case Study 3: Environment

It is essential for smart cities to offer their residents a healthy living environment. This can be achieved by utilizing modern and innovative technologies to enhance the quality of air and water, monitor pollution levels, protect natural habitats, and identify and manage waste effectively [24]. By implementing these measures, a city can create a favorable environment for its inhabitants.

5.5.1 Smart Monitoring of Water Environments

Recently, there is a growing need for sophisticated water management solutions to effectively regulate the quantity and quality of drinking water [139]. Monitoring water is crucial in our world as it permits immediate time monitoring of measurements of water quality as well as efficient resource administration in a smart city to ensure sufficient water access to inhabitants [140]. To address this need, an intelligent solution based on IoT has been suggested to facilitate the monitoring of water zones.

We proposed a SmartWater solution following our presented approach with the centralized method and using cutting-edge technologies, such as sensor clouds, ML, and knowledge reasoning [141]. The proposed solution is presented in Figure 5.10 in detail. The water network is semantically and multi-relationally represented using knowledge graphs, and incremental network embedding is employed to build detailed representations of water objects, with a focus on damaged water zones [142]. A decision process is implemented to develop a smart management plan based on the existing condition of the water zones.
<figure><img src="image_27.png" /><figcaption>FIGURE 5.10: The proposed smart monitoring of water environment scenario following the proposed approach (Centralized method).</figcaption></figure>
5.5.1.1 Dataset

Experiments were conducted utilizing a dataset including water quality data from
several sites to examine the efficiency of the proposed SmartWater management
solution. Temperature, pH, turbidity, dissolved oxygen, conductivity, biological
oxygen demand, nitrate, fecal coliform, and total coliforms are among the 9 metrics
included in this collection of data. Because the dataset lacked information about
trigger events and their associated situations, therefore this was provided by gener-
ating the Water Quality Index (WQI) and classifying water instances depending on
their WQI scores.

5.5.1.2 Data Preprocessing

The information network for the proposed approach was built using the Water
Knowledge Graph (WKG), which consists of three types of nodes: water entity,
event, and action. To represent water entities, the id of locations was used, and
changes in the WQI were utilized to represent the event entities. The management
rules were constructed at random to reflect the actions. The structure of the water
network is reflected in the relations between the entities, while water zones and event
nodes are labeled based on WQI values. The metapath2vec algorithm is utilized to
continuously update the WKG, with a $P: W - E - C - E - W$ guided meta-
path used to generate random walks. The proposed approach utilizes metapath2vec
as an incremental embedding technique to accurately represent both semantic and
structural connections between distinct zone locations. It employs a two-step in-
cremental embedding process, incorporating guided random walks and Skip-Gram
learning to extract node sequences and capture structural relationships. The result-
ing embeddings facilitate tasks such as clustering, classification, anomaly detection,
and decision-making in the water network. Algorithm 3 provides a summary of the
complete process for embedding and classification.
<caption>**Algorithm 3 :** Water zones' embedding</caption>

1: **Input:** $\mathcal{G}$ - Water knowledge graph, $\mathcal{L}$ - Set of labels.
2: **Output:** $\mathcal{V}_l^p$ - Node embeddings in the water network.
3: **Begin**
4:     $\{\mathcal{E}_t^p\}_{t=1}^T \leftarrow \emptyset$
5:     **for each** node $v_i \in V$ **do**
6:         $X = \text{MetaPathRandomWalk}(G, P, v_i, l)$
7:         $X = \text{HeterogeneousSkipGram}(X, k, MP)$;
8:         **for each** node type $t \in T$ **do**
9:             Learn the representations of node $v_i$
10:         $\mathcal{L} \leftarrow$ Minimize relation's inference loss for $v_i$
11:     $\mathcal{V}_l^p \leftarrow \mathcal{V}_l^p \cup v_i$
12:     **end for**
13:     **end for**
14: **Return** $\{\mathcal{V}_l^p\}_{l=1}^{|\mathcal{L}|}$

5.5.1.3 Models Building

The aim of this stage is to compare the classification performance of water zones.
To achieve this, three different ML models were utilized: SVM, LR, and KNN. The
knowledge graph was built using the dataset and node representations were learned
from it. These representations were then fed into the classifiers.

5.5.1.4 Interpretation

Results of the classification of the used models based on the nine parameters of
data are illustrated in Table 5.8, along with the confusion matrices 5.11. The SVM
model demonstrates better performance than the other models. Furthermore, incor-
porating incremental network embedding led to an enhancement in the classification,
demonstrating the effectiveness of latent representations learned through embedding
in an accurate water zone classification. The metapath2vec algorithm was deemed
successful in generating suitable embeddings.
<table><caption>TABLE 5.8: Performance evaluation of water zones classification using embedding techniques</caption><thead><tr><td>Model</td><td>Accuracy</td><td>Precision</td><td>Specificity</td><td>F1-score</td></tr></thead><tbody><tr><td>SVM</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>LR</td><td>99.75</td><td>99.87</td><td>99</td><td>99.76</td></tr><tr><td>KNN</td><td>99.95</td><td>99.97</td><td>99</td><td>99.94</td></tr></tbody></table>

<figure><img src="image_28.png" /><figcaption>FIGURE 5.11: Normalized confusion matrices for the water zones classification without embedding using: (a) SVM, (b) LR, (c) KNN</figcaption></figure>

## 5.6 Limitations of The Proposed Approach

While the proposed approach showcases several advantages, it is crucial to acknowledge its limitations for a comprehensive evaluation. One notable limitation is the dependence on data quality and availability. The accuracy and reliability of the analytics models heavily rely on the quality of the IoT data, and incomplete, noisy, or unreliable data can adversely affect the performance of the approach. Furthermore, limited data availability or data that inadequately represents the target application domain can hinder the approach's effectiveness.

Another significant limitation arises from the computational resource requirements of the approach. The utilization of AI methods, such as ML and DL, can be computationally intensive, posing challenges when deploying the approach on resource-constrained IoT devices or edge computing nodes. Limited processing power, memory, or energy constraints may impede scalability and real-time responsiveness.

Lastly, the approach may necessitate domain expertise and customization, especially in developing accurate and effective ML models for specific IoT applications.
The need for expert knowledge and limited labeled data in certain domains can pose challenges, potentially affecting the applicability and performance of the approach.

## 5.7 Conclusion

This chapter showcases various applications of the proposed approach using different learning settings. Firstly, we demonstrate the use of FL for IoT malware detection and multi-classification. Secondly, we discuss the proposed solutions for healthcare and environmental case studies.

To measure the efficacy of our proposed approach, we evaluate its efficiency among its three key stages: data preprocessing, model building, and interpretation, and the results indicate that our approach performs well.
In this thesis, we proposed a solution using a microservices-based architecture that allows an IoT application to be structured as a collection of fine-grained, loosely coupled, and reusable entities. The proposed solution uses the promising capabilities of centralized and FL to provide intelligent microservices that ensure efficient, flexible, and extensible data analytics. This solution aims to deliver cloud calculations to the edge to reduce runtime and bandwidth bottlenecks in addition to maintaining data privacy. A series of IoT case studies were used to validate the suggested approach. In terms of performance, the findings demonstrated that our suggested strategy beat existing current techniques while also ensuring data confidentiality and lowering transmission costs.

Using an architecture built on microservices to incorporate learning processes has resulted in various benefits, including scalability, flexibility, reliability, modularity, and integration. These advantages have simplified the development, implementation, and management of intricate ML and DL systems in comparison to conventional monolithic architectures.

The following contributions were made in the context of this thesis work:

*   **The proposition of a microservices-based approach that supports the data analytics in IoT application:** We propose a microservices architecture that allows for the creation of reusable and fine-grained entities, which can be easily combined and updated. The process includes a number of stages, starting with data preprocessing, followed by model development and training. The final step involves utilizing the learned models and then fusing decisions to produce insightful results. Each stage is managed by specialized microservices responsible for carrying out specific functions, such as data cleaning, integration, and model evaluation. In summary, our approach illustrates the advantages of utilizing microservices to improve the efficiency and efficacy of data analytics in IoT applications.

*   **Enhance privacy using the FL paradigm:** We utilize FL to ensure data privacy by allowing multiple clients to collaboratively train a model to avoid revealing raw data. Instead, the client's data stays on their devices, and just model updates are exchanged between clients and the cloud server. This approach enhances privacy in several ways. First, it reduces the risk of data breaches or leaks, as the raw data does not go outside of the client machines. Second, it limits the amount of sensitive data that is exposed to the cloud central server, further reducing the danger of unauthorized access. Finally,
FL can also help address regulatory compliance requirements related to data privacy, as it provides a mechanism for organizations to train ML models on sensitive data while preserving privacy.

*   **Improved model generalization using TL:** To enhance our model's generalization, we utilized TL, which allows the model to discover patterns from a diverse set of data. This technique is especially useful when working with smaller datasets, where training a model from scratch can be challenging. TL could also minimize the amount of time and computational resources required to train a model, as the pre-trained model already contains knowledge about the data. By incorporating TL, we were able to improve the performance of our solutions on smaller datasets while reducing training time and resources.

*   **Improved performance using ensemble learning:** To achieve more accurate and comprehensive outcomes, we combined the learned models and decisions. Ensemble learning was employed to enhance our approach by reducing the impact of errors in individual models and leveraging the strengths of multiple models. It also increased the robustness of the analytical model, making it more resistant to overfitting as well as noise in data. By incorporating different types of models, ensemble learning created a diverse set of predictions that captured a wide set of patterns in data. Overall, the utilization of ensemble learning was instrumental in improving our approach by enhancing prediction accuracy, increasing model robustness, and capturing a more diverse range of patterns in the data.

*   **Application and evaluation of the proposed approach:** The proposed approach was enhanced by leveraging the cloud and edge nodes, which support both centralized and distributed learning, making it suitable for a wide range of use cases. To demonstrate the feasibility and validity of the approach, a set of experiments were implemented and conducted on real use-case scenarios. These experiments were designed to test the proposed approach in a practical setting and to validate its effectiveness. Furthermore, a comparative study was conducted, which compared the suggested solutions with related state-of-the-art methods. The comparative study provided valuable insights into the strengths and weaknesses of the proposed approach and highlighted its advantages over other approaches. Overall, these experiments and comparative studies played an important role in providing evidence of the proposed approach's effectiveness and practicality.
In summary, the proposed approach exhibits a significant degree of intelligence. It incorporates cognitive abilities such as perception, action control, and deliberative reasoning through the use of AI methods like ML and DL. By training analytics models using these techniques, the approach demonstrates its ability to understand and process complex IoT data. The approach also follows behavioral principles based on rationality and social norms. It leverages centralized and distributed learning techniques, microservices, and edge computing nodes to ensure efficient data analytics. By adhering to these principles, the approach aims to achieve rational and socially acceptable outcomes.

Furthermore, the approach has the capacity to adapt through learning. It utilizes TL and ensemble learning techniques to improve model generalization and prediction accuracy. This adaptability enables the approach to continually enhance its performance and adapt to changing data patterns and scenarios.

In general, while the proposed approach may not encompass the entirety of human-level intelligence, it demonstrates a considerable degree of intelligence by incorporating cognitive abilities, following behavioral principles, and exhibiting adaptability through learning.

## 6.2 Future Work and Research Directions

Many areas of research can be anticipated from our work. These areas are both methodological and practical:

As part of our future work, we plan to extend our research by integrating other types of DL models, such as RNN and DRL. By incorporating RNNs, we aim to leverage their sequential modeling capabilities, making them suitable for IoT tasks involving time-series data or sequences. This integration will enable us to capture temporal dependencies and effectively process sequential information. Additionally, the inclusion of DRL in our research holds promise for enhancing our understanding of the interaction between DL and reinforcement learning. By combining the power of DNNs with reinforcement learning algorithms, we can develop intelligent agents capable of learning and making decisions in complex and dynamic environments.

By integrating these diverse DL models, it is essential to establish a selection
strategy that ensures an efficient composition of microservices within the architec-
ture. The selection strategy plays a crucial role in determining the appropriate
microservices to include in the composition, considering factors such as functional-
ity, performance, and scalability.

Our models are highly efficient, but it's essential to use them for decision-making to learn about their decision-making process. However, the lack of transparency in these models can lead to mistrust, highlighting the importance of explanations that enhance the overall dependability and comprehensibility of ML systems. Therefore, we aim to concentrate our research on the emerging area of Explainable Deep Learning (XDL), which involves developing innovative approaches and tools to learn a deeper understanding of data, variables, and decisions incorporated in DL models and to make them understandable to humans [143], [144]. Our ultimate goal is to build transparent models that promote trust in their decision-making capabilities.

As a potential avenue for future research, we plan to explore the application of zero-shot learning techniques in our models. Zero-shot learning is a subset of ML that allows models to generalize to new, unseen tasks by leveraging prior knowledge or information [145]. Incorporating zero-shot learning into our models could potentially improve their adaptability and make them more robust in handling novel tasks. Therefore, we see this as an exciting area to investigate in the future.
# List of Publications

My PhD. and research cooperation resulted in multiple international publications, which are mentioned in the following:

## Journal Publications

[P1] Safa Ben Atitallah, Maha Driss, and Henda Ben Ghezela. "FedMicro-IDA: A Federated Learning and Microservices-based Framework for IoT Data Analytics." Internet of Things, Elsevier. (Quartile: Q1, Impact Factor: 5.711) [Accepted on 10/6/2023]

[P2] Maha Driss, Wadii Boulila, Haithem Mezni, Mokhtar Sellami, Safa Benatitallah, Nouf Alharbi. "Enhancing Decision-Making in IoT-based Water Environments through Probabilistic and Evidence Theory-based Knowledge Graph Embedding." Sensors, MDPI, 23(10), pp.4672. (Quartile: Q1, Impact Factor: 3.847).

[P3] Safa Ben Atitallah, Maha Driss, and Iman Almomani. "A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks." Sensors, MDPI, 22, p.4302, 2022. (Quartile: Q1, Impact Factor: 3.847)

[P4] Haithem Mezni, Maha Driss, Wadii Boulila, Safa Ben Atitallah, Mokhtar Selami, and Nouf Alharbi. "Smartwater: A service-oriented and sensor cloud-based framework for smart monitoring of water environments." Remote Sensing, MDPI,
14(4), p. 922, 2022. (Quartile: Q1, Impact Factor: 5.349)

[P5] Maha Driss, Safa Ben Atitallah, Amal Albalawi, and Wadii Boulila. "Req-WSComposer: a novel platform for requirements-driven composition of semantic web services." Journal of Ambient Intelligence and Humanized Computing, Springer, 13(2), p.849-865, 2021. (Quartile: Q1, Impact Factor: 3.662)

[P6] Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa, and Henda Ben Ghézala, "Fusion of Convolutional Neural Networks based on Dempster-Shafer Theory for Automatic Pneumonia Detection from Chest X-Ray Images," International Journal of Imaging Systems and Technology, John Wiley, 32(2), 2021. (Quartile: Q2, Impact Factor: 2.177)

[P7] Safa Ben Atitallah, Maha Driss, Wadii Boulila, Henda Ben Ghézala, "Randomly Initialized Convolutional Neural Network for the Recognition of Covid-19 using X-ray Images." International Journal of Imaging Systems and Technology 32(1), John Wiley, p. 55-73, 2021. (Quartile: Q2, Impact Factor: 2.177)

[P8] Safa Ben Atitallah, Maha Driss, Wadii Boulila, and Henda Ben Ghézala. "Leveraging Deep Learning and IoT Big Data Analytics to Support the Smart Cities Development: Review and Future Directions." Computer Science Review, Elsevier, 38, p.100303, November 2020. (Quartile: Q1, Impact Factor: 8.757)

Conference Publications

[P9] Safa Ben Atitallah, Maha Driss, and Henda Ben Ghézala, "Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning," in 27th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES2023), 2023. (Rank: B, Publisher: Elsevier) [Accepted on 30/5/2023]

[P10] Safa Ben Atitallah, Maha Driss, Wadii Boulila, and Iman Almomani. "An Effective Detection and Classification Approach for DoS Attacks in Wireless Sensor Networks using Deep Transfer Learning Models and Majority Voting," in 14th International Conference on Computational Collective Intelligence (ICCCI2022), 2022. (Rank: B, Publisher: Springer)

[P11] Safa Ben Atitallah, Maha Driss, and Henda Ben Ghézala, "Microservices for
Predictive Analytics in IoT Applications: Benefits, Challenges, and Future Research Directions,” in 26th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES2022), 2022. (Rank: B, Publisher: Elsevier)

[P12] Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa, Nesrine Atitallah, and Henda Ben Ghézala, “An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle Imagery”, in 25th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES2021), 2021. (Rank: B, Publisher: Elsevier)
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