The rapid development of the internet of electric vehicles (IoEV) and the advancement of electric vehicle (EV) charging technology are transforming energy management for both residential and commercial users. Vehicle-to-building (V2B) energy trading is emerging as a groundbreaking approach that incorporates the exchange of energy between EVs and buildings. Despite the fact that V2B energy trading is able to reduce energy costs, main-grid complexity, and greenhouse gas emissions, it faces challenges when it comes to cooperative decision-making, resource efficient computation, and user transaction security. To address these challenges, this study aims to enhance energy exchange efficiency and dynamic energy interactions with enhanced security in urban environments. With these objectives, this paper proposes a novel energy trading method that integrates transfer learning (TL) and blockchain technology. TL makes it possible to adapt the knowledge gathered from vehicle-to-vehicle (V2V) systems to V2B settings, which reduces the computational resources required and boosts the overall efficiency. Blockchain technology, on the other hand, provides a secure framework for transaction verification while granting users enhanced control over their data privacy. We demonstrate the effectiveness of our proposed technique using detailed simulations conducted with real-world data. Our simulation results indicate that the proposed approach improves the convergence speed by around 50% compared to training from scratch, while buildings achieve up to 38% higher profits relative to scenarios without our proposed strategy. We also simulate the system model using the Ethereum blockchain platform to determine its real-world feasibility. These experiments demonstrate that the system has the potential to facilitate efficient energy trading to ensure user security and economically beneficial transactions.

Quishing, a novel form of phishing that exploits QR codes, has emerged as a growing cybersecurity threat. Attackers embed malicious URLs within QR codes to deceive users into accessing fraudulent websites or executing harmful actions. Given the increasing reliance on QR codes in banking, retail and public services, the need for robust detection mechanisms is critical. This paper presents a machine learning-based approach to detecting malicious URLs within QR codes, integrating lexical and behavioral analysis to improve classification accuracy. We evaluated multiple models, including Decision Trees, Support Vector Machines (SVM), Random Forest, and Long-Short-Term Memory (LSTM) networks. Experimental results indicate that the Random Forest model achieves superior performance in terms of detection accuracy and computational efficiency, making it suitable for real-time deployment. The findings contribute to the advancement of QR code security and malicious URL detection, providing practical solutions for cybersecurity applications.

Modern edge cloud platforms must efficiently deploy and route containerized microservice DAGs under strict latency and cost constraints, while adapting to rapidly changing workloads and infrastructure states. Deep Reinforcement Learning (DRL) schedulers adapt well to dynamics but often lack semantic awareness of service intent and task dependencies, resulting in suboptimal decisions in unseen scenarios. To overcome these limitations, we introduce a Retrieval-Augmented Generation-assisted DRL (RAG-DRL) framework that integrates a lightweight DRL agent with a graph-based RAG module powered by a partially frozen LLM. A dynamic memory graph encodes contextual information such as node resources, network latencies, and SLA feedback. The LLM retrieves relevant historical deployments and current service intents to generate soft placement plans and reward estimates, which guide the DRL agent. These priors accelerate convergence, improve generalization across diverse conditions, and ensure real-time responsiveness. Evaluations on a realistic urban-scale edge cloud testbed confirm that RAG-DRL significantly reduces SLA violations, end-to-end latency, and resource imbalance, outperforming modern container-based schedulers. Our framework converges faster, maintains latency below 65 ms on scale, limits SLA violations to 12% under heavy load, and achieves 90 % resource utilization with balanced distribution.

Federated learning (FL) gained importance in sensitive Internet of Things (IoT) environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, FL shifts data control away from the server, exposing it to non-independent and identically distributed (non-IID) problems caused by biased clients (IoT devices). This hinders the learning process by increasing execution time and cost. Current solutions alter the FL structure or compromise privacy by offloading clients’ raw data to an external server. To mitigate these limitations, this article proposes a solution to the non-IID problem by introducing an initialization phase, orchestrated by the server, that constructs high-quality initial models. These models can boost FL accuracy and convergence, regardless of whether IoT participants exhibit non-IID properties. Our proposed initialization scheme involves clients training over the same model sequentially, lessening the impact of aggregation, a primary cause of model degradation in federated approaches. Furthermore, a regulator algorithm deployed on the server maintains model integrity and mitigates catastrophic forgetting, enhanced by a client selection process that emphasizes the compatibility of IoT clients to cooperate effectively. Moreover, we devise an optimization scheme based on clustering and genetic algorithms to reduce the selection time while ensuring optimal performance in IoT networks. Experiments on MNIST, KDD, and CIFAR10 data sets show promising results in terms of initial model resiliency against catastrophic forgetting and non-IID settings. Additionally, our findings suggest that our approach can significantly enhance FL training in IoT applications by achieving 40% higher initialization accuracy and a 20% average improvement in end results compared to conventional methods, all while reducing computation time by 80% compared to similar approaches.

While the Internet of Vehicles (IoV) can revolutionize transportation systems through intelligent connectivity, a critical challenge in realizing this potential lies in ensuring efficient channel allocation in the IoV ecosystem, particularly considering dynamic channel conditions and adversarial jamming exacerbated by the emergence of artificial intelligence (AI)-based jamming. To address these challenges, in this study, we use distributed edge intelligence (DEI) to propose a distributed channel access mechanism for the vehicle-to-infrastructure (V2I) mode of IoV networks. Specifically, using an actor-critic-based multiagent reinforcement learning (MARL) framework with a common critic, we model the distributed channel access problem in V2I communications under varying channel conditions and an intelligent jamming device$(\tt {iJD})$interference as a decentralized partially observable stochastic game (Dec-POSG). Furthermore, by addressing challenges, such as partial observations, nonstationarity, and credit assignment, our proposed approach fosters collaboration among intelligent vehicles ($\tt {iV}$s) without direct communication. In addition, our unique counterfactual reasoning-aided action evaluation mechanism and a novel utility function design enable the$\tt {iV}$s to learn mixed collaborative-competitive channel access policies, thereby enhancing channel utilization, mitigating the impact of the$\tt {iJD}$, and improving the network’s sum cross-layer achievable rate (SCLAR).

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the availability of FL clients fluctuates as devices may become inaccessible, leading to inefficient client selection and reduced model performance. Current solutions often fail to adapt quickly to these changes, creating a gap in achieving real-time client availability and efficient data utilization. To address this, we propose a Deep Reinforcement Learning (DRL) On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing DRL, targets client availability and selection while considering data shifts and container deployment complexities. It employs an autonomous end-to-end approach for handling model deployment and client selection. The DRL strategy leverages a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner to optimize decision-making. The designed cost functions account for the complexity of dynamic client deployment and selection, ensuring effective resource management and service reliability. Simulated tests show that our architecture can easily adapt to changes in the environment and respond to On-Demand requests while reducing the number of learning rounds used by 20-50 % compared with existing approaches. This highlights its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and traditional reinforcement learning methods.

In recent years, the rapid advancement of wireless communication technologies, particularly in the development of sixth-generation networks, brought about challenges in spectrum efficiency, security, and reliability. Machine learning-based automatic modulation classification (AMC) plays a critical role in addressing these challenges by enabling efficient signal classification in dynamic environments. However, such systems remain vulnerable to adversarial attacks, which can induce machine learning-based systems into making mistakes and, by doing so, compromise applications that rely on them. Accordingly, in this study, we propose a robust AMC framework based on Bayesian neural networks (BNN) to mitigate the impact of adversarial attacks. Our approach uses a regularization term on the weight variance of the BNN to reduce the likelihood of extreme weight values, thereby enhancing model stability in adversarial settings. We also incorporate the Sinh-Arcsinh Gaussian distribution as a flexible prior to control skewness and tail behavior, thus improving the trade-off between robustness and accuracy. Experimental evaluations against common white-box adversarial attacks, such as fast gradient sign method (FGSM), projected gradient descent (PGD), and automatic PGD (Auto-PGD), demonstrate that our proposed model outperforms conventional AMC models, achieving greater resilience in low perturbation-to-noise ratio conditions. Taken together, these findings highlight the potential of Bayesian methods in developing more secure and reliable intelligent wireless communication systems.

Federated learning (FL) is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system’s integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in FL by considering the trust needs of both the client and the server. Our approach entails: 1) creating preference functions for servers and clients, allowing them to rank each other based on trust scores; 2) establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers; 3) assigning credibility scores to recommending devices for better server trustworthiness measurement; 4) developing a trust assessment mechanism for smart devices using a statistical interquartile range (IQR) method; and 5) designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing nontrustworthy clients in the system.

In this article, we tackle the network delays in the Internet of Things (IoT) for an enhanced Quality of Service (QoS) through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in QoS for IoT applications and may even disrupt time-critical functions. This article addresses the challenge of establishing fog federations, which are designed to enhance QoS. However, instabilities within these federations can lead to the withdrawal of providers, thereby diminishing federation profitability and expected QoS. Additionally, the techniques used to form federations could potentially pose data leakage risks to end-users whose data is involved in the process. In response, we propose a stable and comprehensive federated fog architecture that considers federated network profiling of the environment to enhance the QoS for IoT applications. This article introduces a decentralized evolutionary game theoretic algorithm built on the top of a genetic algorithm mechanism that addresses the fog federation formation issue. Furthermore, we present a decentralized federated learning algorithm that predicts the QoS between fog servers without the need to expose users’ location to external entities. Such a predictor module enhances the decision-making process when allocating resources during the federation formation phases without exposing the data privacy of the users/servers. Notably, our approach demonstrates superior stability and improved QoS when compared to other benchmark approaches.

In edge computing environments, efficient computation resource management is crucial for optimizing service allocation to hosts in the form of containers. These environments experience dynamic user demands and high mobility, making traditional static and heuristic-based methods inadequate for handling such complexity and variability. Deep Reinforcement Learning (DRL) offers a more adaptable solution, capable of responding to these dynamic conditions. However, existing DRL methods face challenges such as high reward variability, slow convergence, and difficulties in incorporating user mobility and rapidly changing environmental configurations. To overcome these challenges, we propose a novel DRL framework for computation resource optimization at the edge layer. This framework leverages a customized Markov Decision Process (MDP) and Proximal Policy Optimization (PPO), integrating a Graph Convolutional Transformer (GCT). By combining Graph Convolutional Networks (GCN) with Transformer encoders, the GCT introduces a spatio-temporal reward-shaping mechanism that enhances the agent's ability to select hosts and assign services efficiently in real time while minimizing the overload. Our approach significantly enhances the speed and accuracy of resource allocation, achieving, on average across two datasets, a 30% reduction in convergence time, a 25% increase in total accumulated rewards, and a 35% improvement in service allocation efficiency compared to standard DRL methods and existing reward-shaping techniques. Our method was validated using two real-world datasets, MOBILE DATA CHALLENGE (MDC) and Shanghai Telecom, and was compared against standard DRL models, reward-shaping baselines, and heuristic methods. • Proposing a DRL framework that integrates reward shaping for resource management. • Introducing a novel MDP design that considers the dynamic nature of the users. • Presenting a novel reward shaping mechanism, incorporating GCN and transformers.

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

Rapid advancements in deep learning (DL) and the availability of the large data sets have made the adoption of DL highly appealing across various fields. Wireless communication systems, including future 6G systems are anticipated to incorporate intelligent components like automatic modulation classification (AMC) for the cognitive radio and dynamic spectrum access. However, DL-based AMC models are susceptible to the adversarial attacks, which consist of crafted perturbations that aim to alternate the decision of a victim model. This study focuses on investigating and uncovering modern modulation classifiers’ vulnerability to the adversarial threats. Though attacks of this nature inherently jeopardize DL-based classifiers, contemporary attack methods typically exhibit diminished impact at the lower perturbation levels. Therefore, we introduce a novel attack approach that exploits the Riemannian manifold properties of the intricate neural networks, yielding adversarial samples with heightened efficacy at the lower perturbation powers. We thoroughly evaluate how effective various defense techniques are and demonstrate our proposed attack method’s ability to thwart them. The findings of this study shed light on the limitations and vulnerabilities of the DL-based AMC models in the face of the adversarial attacks. By addressing these challenges, we can enhance the robustness and security of these models, and pave the way for their reliable deployment in practical wireless communication systems, including the future 6G networks.

Federated learning (FL) is a promising approach for processing on-board data in vehicular networks due to its distributed nature and its ability to accurately and efficiently handle the large amount of sensed data. However, training and transmitting the model parameters during FL process can consume a significant amount of energy and time, which is not suitable for applications with strict real-time requirements. Moreover, the dynamicity of the vehicular network, as well as the varying capabilities of each vehicle, can impact the performance of the training process, bringing to the forefront the optimization of the participants selection and their resources. In this paper, we propose VOC-FL, a Vehicular-based Offloading and Clustering framework supported by FL. The proposed scheme bypasses communication bottlenecks by enabling groups of vehicles to train models simultaneously, with only the Cluster Head (CH) sending the aggregated results of each cluster to the roadside units for further processing. To form the clusters, we select a CH for each cluster based on multiple metrics, including stability, computational resources, bandwidth, and network topology. Moreover, the CH runs an offloading strategy that allows struggling nodes with limited computational resources to offload their tasks to other nodes with enough resources within the cluster, enabling efficient and effective use of resources.

Network delays cause a reduction in the Quality-of-Service (QoS) for Internet of Things (IoT) applications, and even render time-critical applications inoperative. The paper tackles the problem of forming fog federations that aim to improve the QoS. However, instabilities within fog federations might cause some providers to withdraw from the federation, and thus decrease the profit of the federations and the expected QoS. Moreover, federation formation techniques could potentially create privacy risks for end-users whose data is utilized in the process. This paper introduces a decentralized evolutionary game theoretic algorithm that tackles the problem of fog federation formation, as well as, providing a decentralized privacy-aware federated learning algorithm that predicts the QoS between fog servers for optimizing the formation procedure. The devised method provides better stability and increased QoS when compared to other benchmarks.

With an innovative door opened for a new era of Machine Learning, Federated Learning (FL) is now revolutionizing Artificial Intelligence. It exploits both decentralized data and decentralized computation to preserve user privacy. Albeit its popularity and being the most widely used framework nowadays, FL becomes a sub-optimal solution when the convergence of the global model occurs at a slow pace, which exacerbates the communication bottlenecks. To address this challenge, we propose in this paper CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. First, we partition the clients into different groups, where sub-aggregations of the clients models are performed at each cluster before the global aggregation. Second, we study the computing resources of the clients, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.

In this paper, we increase the availability and integration of devices and models together in the learning process to enhance the convergence of federated learning (FL) models. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be while serving multiple FL models. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process while supporting multiple models. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The performed experiments using the Mobile Data Challenge (MDC), MNIST, KDD datasets, and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients with less discarded rounds and more available data of each running FL application.

To cope with the increasing growth of last-mile delivery, crowdsourcing last-mile delivery has been adopted as a flexible and cost-efficient way to deliver parcels quickly and efficiently. However, some potential downsides to crowdsourcing last-mile delivery include concerns about safety, reliability, and transparency. Therefore, blockchain has been adopted to promote transparency in the last-mile delivery process. Despite the impact of a worker’s reputation on task completion, existing works do not offer a traceable and transparent reputation metric for lastmile delivery workers. This paper proposes a blockchain-based framework for reputation management in crowdsourced last-mile delivery. The proposed framework is designed as smart contracts that maintain and update crowdsourced workers’ information, mainly reputation, in a transparent and traceable manner. In addition, the framework allows requesters to create their delivery tasks and workers to get allocated available tasks. The proposed framework uses Solidity to interact with smart contracts for requesters and workers. The cost analysis demonstrates the proposed framework’s feasibility and cost efficiency.

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals, such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices, such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multiparty architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing, and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: 1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog, and cloud computing layers; 2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of subtechniques; and 3) propose a comprehensive cybersecurity framework that combines the concepts of explainable artificial intelligence (XAI), federated learning, game theory, and social psychology to offer future IoT systems a strong protection against cyberattacks.

In this article, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, FL, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each data set over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this article, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology, such as Docker, to build efficient environments using Internet of Things and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. A multiobjective optimization problem representing the client and model deployment is solved using the genetic algorithm (GA) due to its evolutionary strategy. The performed experiments using the mobile data challenge (MDC) data set and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multiaccess edge computing (MEC), which consists of distributing the edge servers in the proximity of end users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This article addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space, and the penalty function in the design of the underlying Markov decision process (MDP) for solving our problem. This modeling makes the temporal difference (TD) learning applicable for a large-scale real-world problem while minimizing the cost of network design. We introduce the TD$(\lambda)$with eligibility traces for minimizing the cost (TDMC) algorithm, in addition to$Q$-learning for the same problem (QMC) when$\lambda =0$. Furthermore, we discuss the impact of state representation, action space, and penalty function on the convergence of each model. Extensive experiments using real-world data sets from Shanghai Telecommunication and Citywide Public Computer Centers demonstrate that in the light of an efficient model, TDMC/QMC are able to find the actions that are the source of lower delayed penalty. The reported results show that our algorithm outperforms the other benchmarks by creating a tradeoff among multiple objectives.

Conventional systems are usually constrained to store data in a centralized location. This restriction has either precluded sensitive data from being shared or put its privacy on the line. Alternatively, federated learning (FL) has emerged as a promising privacy-preserving paradigm for exchanging model parameters instead of private data of Internet of Things (IoT) devices known as clients. FL trains a global model by communicating local models generated by selected clients throughout many communication rounds until ensuring high learning performance. In these settings, the FL performance highly depends on selecting the best available clients. This process is strongly related to the quality of their models and their training data. Such selection-based schemes have not been explored yet, particularly regarding participating clients having high-quality data yet with limited resources. To address these challenges, we propose in this article FedAUR, a novel approach for an adaptive upgrade of clients resources in FL. We first introduce a method to measure how a locally generated model affects and improves the global model if selected for aggregation without revealing raw data. Next, based on the significance of each client parameters and the resources of their devices, we design a selection scheme that manages and distributes available resources on the server among the appropriate subset of clients. This client selection and resource allocation problem is thus formulated as an optimization problem, where the purpose is to discover and train in each round the maximum number of samples with the highest quality in order to target the desired performance. Moreover, we present a Kubernetes-based prototype that we implemented to evaluate the performance of the proposed approach.

Federated learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things (IoT) devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this articleFedMint, an intelligent client selection approach for FL on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: 1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors, such as accuracy and price; 2) intelligent matching algorithms that take into account the preferences of both parties in their design; and 3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against theVanillaFLselection process as well as other state-of-the-art approach and showcase the superiority of our proposal.

In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN-RS embeds a CNN trained on computed labels using the message passing mechanism of the Hidden Markov Model. The CNN processes images or graphs of the environment to predict the shaping values. Recent work on reward shaping still has limitations towards training on a representation of the Markov Decision Process (MDP) and building an estimate of the transition matrix . The advantage of VIN-RS is to construct an effective potential function from an estimated MDP while automatically inferring the environment transition matrix. The proposed VIN-RS estimates the transition matrix through a self-learned convolution filter while extracting environment details from the input frames or sampled graphs. Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS. Experiments are performed on tabular games, Atari 2600 and MuJoCo, for discrete and continuous action space. Our results illustrate promising improvements in the learning speed and maximum cumulative reward compared to the state-of-the-art. The improvement achieved by VIN-RS can only be observed for some of the games due to the underlying nature of some environments. In terms of the studied MuJoCo games, there is on average an increase of 30% in the maximum reward reached during early stages of learning.

Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the future vision of globalization. While IoT is about sensing data from physical devices, DTs reflect their digital representation and enable optimized decision-making by tightly integrating Artificial Intelligence (AI). Although swiftly growing, DTs are raising new challenges in privacy concerns, which are nowadays addressed by FL. However, the limited IoT resources, the communication overhead, and the lack of trust among clients are major obstacles that hinder the effectiveness of learning systems. In this paper, we design a new IoT-based architecture empowered by DT to improve the efficiencies of limited-resources devices. On top of this architecture, we leverage FL to construct the DT models. We further propose CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. Particularly, we study the computing resources of the clients and the quality of their models, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.

Federated fog computing is an answer for horizontally upscaling fog resources to improve the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic nature of some IoT’s crucial components, such as the ones of Internet of Vehicles (IoV), may hinder the QoS improvement and result in its deterioration instead. Specifically, delays can occur due to the unoptimized distribution of services and unbalanced network traffic loads on the fog nodes. The current federated fog architectures ignore the mobility of users during the formation of fog federations. In this work, we present an adaptive and efficient fog federation formation scheme using game theory according to the service requirements. The problem formulation in terms of forming the federations and offloading requests among fog members is formulated as an integer program, then modeled as a Hedonic game. We adopt the Merge & Split as a formation technique, where the federations that are not satisfied in terms of QoS merge with other federations that would enhance the service performance. Our adaptive fog federation formation mechanism is designed to cope with the environmental changes in the IoV paradigm. Experimental evaluation shows that our framework can acquire better QoS and lower time to form the federations compared to the literature.

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from Internet of Things (IoT) devices, such as radars, sensors, and actuators. Consequently, overwhelming the existing infrastructure, namely cloud, and Road Side Units (RSU), reduces the quality of service (QoS) experienced by vehicular users. Accordingly, this paper contributes in proposing a new architecture for using Unmanned Ariel Vehicles (UAVs) and On-Boarding Units (OBUs) working in collaboration to achieve a significantly improved QoS. The proposed framework offers an end-to-end solution for master election, cluster management and recovery, vehicle selection, service placement, and accurate localization of vehicles. A QoS improvement is possible through an efficient cluster formation and placement solution that assigns lightweight services, as containers, to OBUs and UAVs while meeting various objectives. The efficiency of the proposed scheme originates from the use of the evolutionary Memetic Algorithm that 1) respects the mobility and energy constraints of UAVs and OBUs, 2) meets the user demands, and 3) uses machine learning for the accurate localization of vehicles. Our experiments using the Mininet-WiFi and SUMO simulators show at least 30% improvement in terms of QoS compared to a state-of-the-art solution.

In this article, we propose a new paradigm of Federated Learning (FL) for Internet of Things (IoT) devices calledCoalitional Federated Learning. The proposed paradigm aims to address the challenges of (1) non-independent and identically distributed (non-IID) data across clients; (2) communication overhead due to the large number of messages exchanged between the server and clients; and (3) selfish clients that seek to obtain the latest global models without efficiently contributing to the training of the FL model. Our novel paradigm consists of three main components, i.e., (1) client-to-client trust establishment mechanism that relies on subjective and objective sources to enable clients to establish credible trust relationships toward one another; (2) trust-enabled coalitional game to enable clients to autonomously form harmonious coalitions of FL trainers; and (3) coalitional federated learning in which multiple local aggregations take place at the level of each coalition to mitigate the problems of non-IID data and communication bottleneck. Extensive experiments suggest that our solution outperforms both the standard vanilla FL approach and one state-of-the-art trust-based FL approach in terms of increasing the accuracy of the global FL model and decreasing the presence of selfish devices participating in the training.

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federations of fog providers for task offloading purposes. Nonetheless, a few challenges remain a burden for the formation of the federations. The formation mechanisms used to structure the federations of providers are still not fully stable. This causes a problem because a structureless federation can lead to an underperforming infrastructure. Furthermore, most of the literature ignored the honesty metrics of the providers and how trustworthy they are in allocating the agreed-upon resources for processing the tasks. Moreover, adopting a central reputation mechanism is questionable in terms of reliability due to many complications including the lack of consensus. In this work, we develop a Blockchain-based reputation mechanism for assisting the formation of fog federations for IoV applications. Our mechanism comprises on-chain smart contracts for storing and manipulating the providers’ reputations, and an off-chain Hedonic-based formation process that considers the parameters extracted from the chain to build the federations. We develop smart contracts using Solidity and deploy them on the Ethereum Blockchain. We test our mechanism using the EUA dataset as a proof of concept and compare it to other works in the literature. The results obtained show that our approach is able to enhance the overall payoff and quality of service in the IoV paradigm.

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome resulting from alterations in the embryological brain pre-birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior, in addition to specific behavioral traits, deteriorating their social behavior and interaction within their community. Moreover, medical research has proved that ASD affects the facial features of its patients, making the syndrome recognizable from distinctive signs within an individual's face. Given that as a motivation behind our work, we propose a novel privacy-preserving FL model, in order to predict ASD in a certain individual based on their behavioral traits or facial features, while respecting patient data privacy, as ASD data is medical and hence sensitive to leakage. After training behavioral and facial image data on Federated Machine Learning (FL) models, promising results are achieved, with 70% accuracy for prediction of ASD according to behavioral traits in a federated learning private environment, and a 62% accuracy is reached for prediction of ASD given an image of the patient's face.

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality and resources across the participants. To overcome this problem, we propose an intelligent client selection approach for federated learning on IoT devices using matching game theory. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several criteria such as accuracy and price, and (2) intelligent matching algorithms that take into account the preferences of both parties in their design. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.

Fog computing extends the concept of cloud computing by allowing services, embedded into virtual machines or containers, to be placed at the edge of the network in the proximity of the end devices. However, due to the huge increase in the number of user requests, placing containers onto fog devices becomes a challenging task. In this work, we address the problem of large-scale container placement in fog computing environments. We propose a machine learning-based K-means clustering solution, which we integrate into the Genetic Algorithm (GA) to improve the selection of the initial population. We first formulate the container placement problem as a multi-objective optimization model with several (conflicting) objectives and then propose a cluster-based GA approach to solve the problem in an efficient manner. Simulation results suggest that our solution outperforms one state-of-the-art approach in terms of effectiveness and efficiency.

The high need of real-time vehicular applications for self-driving cars to maintain service availability and reachability, and to process huge amount of generated data within a small amount of time, rise the need to improve the vehicular network infrastructure. Fog computing has been introduced to reduce the amount of data sent to cloud by bringing processing power near the edge and reducing latency. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Reinforcement Learning, Kubeadm Clustering, Docker Containerization, Istio service mesh, and micro-services technologies. We propose in our scheme (1) a service mesh architecture that manages communication between multiple micro services across different clusters and tackle inter-service communication, (2) a Reinforcement Learning model deployed on RSUs to predict on-demand placement of microservices. Experiments and simulations show that our method is more efficient in deploying microservices using the reinforcement learning model than other current strategies in the literature. Given that only needed microservices are deployed in limited resource cluster, mesh network shows an improvement in the deployment time and inter-service communication across clusters.

The broad applicability of Internet of Things (IoT) would truly enable the pervasiveness of smart devices for sensing data. In this context, achieving service personalization requires collecting sensitive data about users. That yields to privacy concerns due to the possibility of abusing the data through unauthorized access. Moreover, IoT devices have limited computing resources, making them difficult to perform heavy protection mechanisms. Despite several existing solutions for privacy protection, they were not designed to run on limited resources in large scale environment. In addition, existing access control solutions, including XACML, are heavy to run on resource constraint devices and lack behavior-based customization of user privacy where users have little to no control over their private data. In this regard, we address the aforementioned problems by proposing LP-SBA-XACML, which embeds an efficient and lightweight semantics-based scheme targeting user privacy and providing efficient policy evaluation. LP-SBA-XACML is a scalable and lightweight solution suitable for the IoT context while preserving the assumptions of XACML. Moreover, an intelligent model for real-time behavior/activity prediction is integrated to systematically customize user’s privacy and services. Experiments conducted on synthetic and real-life scenarios demonstrate the feasibility and relevance of our proposed framework within a mobile IoT resource-constrained environment.

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

Federated learning using fog computing can suffer from the dynamic behavior of some of the participants in its training process, especially in Internet-of-Vehicles where vehicles are the targeted participants. For instance, the fog might not be able to cope with the vehicles’ demands in some areas due to resource shortages when the vehicles gather for events, or due to traffic congestion. Moreover, the vehicles are exposed to unintentionally leaving the fog coverage area which can result in the task being dropped as the communications between the server and the vehicles weaken. The aforementioned limitations can affect the federated learning model accuracy for critical applications, such as autonomous driving, where the model inference could influence road safety. Recent works in the literature have addressed some of these problems through active sampling techniques, however, they suffer from many complications in terms of stability, scalability, and efficiency of managing the available resources. To address these limitations, we propose a horizontal-based federated learning architecture, empowered by fog federations, devised for the mobile environment. In our architecture, fog computing providers form stable fog federations using a Hedonic game-theoretical model to expand their geographical footprints. Hence, providers belonging to the same federations can migrate services upon demand in order to cope with the federated learning requirements in an adaptive fashion. We conduct the experiments using a road traffic signs dataset modeled with intermodal traffic systems. The simulation results show that the proposed model can achieve better accuracy and quality of service than other models presented in the literature.

The increasing number of Internet of Things (IoT) devices necessitates the need for a more substantial fog computing infrastructure to support the users’ demand for services. In this context, the placement problem consists of selecting fog resources and mapping services to these resources. This problem is particularly challenging due to the dynamic changes in both users’ demand and available fog resources. Existing solutions utilize on-demand fog formation and periodic container placement using heuristics due to the NP-hardness of the problem. Unfortunately, constant updates of services are time consuming in terms of environment setup, especially when required services and available fog nodes are changing. Therefore, due to the need for fast and proactive service updates to meet users’ demand, and the complexity of the container placement problem, we propose in this article a Deep Reinforcement Learning (DRL) solution, named Intelligent Fog and Service Placement (IFSP), to perform instantaneous placement decisions proactively. By proactively, we mean making placement decisions before demands occur. The DRL-based IFSP is developed through a scalable Markov Decision Process (MDP) design. To address the long learning time for DRL to converge, and the high volume of errors needed to explore, we also propose a novel end-to-end architecture utilizing a service scheduler and a bootstrapper. on the cloud. Our scheduler and bootstrapper perform offline learning on users’ demand recorded in server logs. Through experiments and simulations performed on the NASA server logs and Google Cluster Trace datasets, we explore the ability of IFSP to perform efficient placement and overcome the above mentioned DRL limitations. We also show the ability of IFSP to adapt to changes in the environment and improve the Quality of Service (QoS) compared to state-of-the-art-heuristic and DRL solutions.

Internet of Vehicles and vehicular networks have been compelling targets for malicious security attacks where several intrusion detection solutions have been proposed for protecting them. Nonetheless, their main problem lies in their heavy computation, which makes them unsuitable for next-generation artificial intelligence-powered self-driving vehicles whose computational power needs to be primarily reserved for real-time driving decisions. To address this challenge, several approaches have been lately presented to take advantage of the cloud computing for offloading intrusion detection tasks to central cloud servers, thus reducing storage and processing costs on vehicles. However, centralized cloud computing entails high latency on intrusion detection related data transmission and plays against its adoption in delay-critical intelligent applications. In this context, this article proposes a vehicular-edge computing (VEC) fog-enabled scheme allowing offloading intrusion detection tasks to federated vehicle nodes located within nearby formed ad hoc vehicular fog to be cooperatively executed with minimal latency. The problem has been formulated as a multiobjective optimization model and solved using a genetic algorithm maximizing offloading survivability in the presence of high mobility and minimizing computation execution time and energy consumption. Experiments performed on resource-constrained devices within actual ad hoc fog environment illustrate that our solution significantly reduces the execution time of the detection process while maximizing the offloading survivability under different real-life scenarios.

As an alternative centralized systems, which may prevent data to be stored in a central repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game changer addressing both privacy and cooperative learning. It succeeds in keeping training data on the devices, while sharing locally computed then globally aggregated models throughout several communication rounds. The selection of clients participating in FL process is currently at complete/quasi randomness. However, the heterogeneity of the client devices within Internet-of-Things environment and their limited communication and computation resources might fail to complete the training task, which may lead to many discarded learning rounds affecting the model accuracy. In this article, we propose FedMCCS, a multicriteria-based approach for client selection in FL. All of the CPU, memory, energy, and time are considered for the clients resources to predict whether they are able to perform the FL task. Particularly, in each round, the number of clients in FedMCCS is maximized to the utmost, while considering each client resources and its capability to successfully train and send the needed updates. The conducted experiments show that FedMCCS outperforms the other approaches by: 1) reducing the number of communication rounds to reach the intended accuracy; 2) maximizing the number of clients; 3) handling the least number of discarded rounds; and 4) optimizing the network traffic.

Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.

Cloud-based systems are subject to various attack types launched by Virtual Machines (VMs) manipulated by attackers having different goals and skills. The existing detection and defense mechanisms might be suitable for simple attack environments but become ineffective when the system faces advanced attack scenarios wherein simultaneous attacks of different types are involved. This is because these mechanisms overlook the attackers' strategies in the detection system's design, ignore the system's resource constraints, and lack sufficient knowledge about the attackers' types and abilities. To address these shortcomings, we propose a repeated Bayesian Stackelberg game consisting of the following phases: risk assessment framework that identifies the VMs' risk levels, live-migration-based defense mechanism that protects services from being successful targets for attackers, machine-learning-based technique that collects malicious data from VMs using honeypots and employs one-class Support Vector Machine to learn the attackers' types distributions, and resource-aware Bayesian Stackelberg game that provides the hypervisor with the detection load's optimal distribution over VMs that maximizes the detection of multi-type attacks. Experiments conducted using Amazon's datacenter and Amazon Web Services honeypot data reveal that our solution maximizes the detection, minimizes the number of attacked services, and runs efficiently compared to the state-of-the-art detection and defense strategies, namely Collabra, probabilistic migration, Stackelberg, maxmin, and fair allocation.

Currently, researchers have motivated a vision of 6G for empowering the new generation of the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G, more computing resources are required, a problem that is dealt with by Mobile Edge Computing (MEC). However, due to the dynamic change of service demands from various locations, the limitation of available computing resources of MEC, and the increase in the number and complexity of IoE services, intelligent resource provisioning for multiple applications is vital. To address this challenging issue, we propose in this paper IScaler, a novel intelligent and proactive IoE resource scaling and service placement solution. IScaler is tailored for MEC and benefits from the new advancements in Deep Reinforcement Learning (DRL). Multiple requirements are considered in the design of IScaler's Markov Decision Process. These requirements include the prediction of the resource usage of scaled applications, the prediction of available resources by hosting servers, performing combined horizontal and vertical scaling, as well as making service placement decisions. The use of DRL to solve this problem raises several challenges that prevent the realization of IScaler's full potential, including exploration errors and long learning time. These challenges are tackled by proposing an architecture that embeds an Intelligent Scaling and Placement module (ISP). ISP utilizes IScaler and an optimizer based on heuristics as a bootstrapper and backup. Finally, we use the Google Cluster Usage Trace dataset to perform real-life simulations and illustrate the effectiveness of IScaler's multi-application autonomous resource provisioning.

Multi-persona mobile computing has begun to make its way to determine the battle about practical strategy for adopting personal devices in workplace. Though its competency, multi-persona performance and viability are critically threatened by the limited resources of mobile devices. In recent years, mobile edge computing (MEC) has risen as promising paradigm within the internet of things era bringing benefits to the proximity of mobile terminals, leveraging intelligent computations offloading services to address the severity of their resource scarcity. Yet, embracing mobile edge-based services to augment personas resources and performance raises new concerns including determining what computations to offload for serving the highest number of mobile devices and reducing the remote execution fees imposed on the institution. In this context, we propose new cost-effective MEC-based solution to address these issues. We develop two-level multi-objective optimization realized through an intelligent offloading decision model able to settle both concerns, by minimizing processing, memory and energy while augmenting virtual mobile instances performance on a wide range of physical devices with minimal offloading service fees. We also propose a redesigned smart genetic-based method able to accelerate and reduce the overhead of offloading decision evaluation. Extensive analysis is performed and the results show that our proposition can get more quickly the offloading strategy than other schemes. The results also demonstrate the ability to enforce the virtual mobile devices by reducing local processing, memory usage, energy consumption and execution time along with acceptable minimal additional fees compared to other techniques.

In the age of cloud-native implementation both monitoring and automated orchestration plays an important role for managing these applications' life cycle. There are lot of available monitoring tools which are able to monitor these implementations but they lack the application related metrics and also the automated orchestration is still at a premature stage. In this article we are proposing a framework that takes application related metrics along with the absolute and relative metrics and pro-actively performs automated orchestration using machine learning for scalability.

This work targets the performance analysis of optical spatial modulation (OSM) for free-space optical (FSO) communication systems under the exact Poisson photon-counting detection model. We derive the error probability in the case of receivers equipped with multiple apertures where we analyze and compare different combining schemes. This paper also focuses on the energy harvesting (EH) in OSM FSO systems. In particular, we evaluate the error performance and EH levels with the conventional pulse position modulation (PPM) and with the more recent EH-efficient pulse gap modulation (PGM). We also advise novel averaging-based and switching-based EH architectures for multi-aperture receivers and we compare the EH capabilities of these schemes.

The massive increase of IoT connected devices is imposing various challenges at different dimensions due to the constrained capabilities of devices and huge requirements of applications. Attributed to the benefits it offers in terms of time and cost, pushing security solutions to the network edge has attracted tremendous interest. Most of the hashing algorithms are centralized focusing on optimizing hashing techniques while considering enough processing and power capabilities. However, hashing algorithms in the context of IoT world suffer from the limitations of devices in terms of battery power and computing capabilities, thus distributed hashing becomes a necessity to enable it within IoT environment. In this paper, we introduce a trust-aware based model that provides efficient and trusted distribution of hashing computation among mobile edge resources. We formulate the distribution model as an integer linear programming problem while taking into consideration various parameters and constraints. Experimental results explore the efficiency of our proposed approach with respect to the literature.

Several studies leverage fog computing as a solution to overcome cloud delays, including computation, network, and data storage. Along with the increase in demands for computing resources in fog infrastructures, heterogeneous fog devices are used towards forming highly available clusters. Existing approaches support the use of heterogeneous fogs and enable dynamic updates and management of services through containerization and orchestration technologies. However, none of the existing works proposed a proactive solution to horizontally scale these resources based on the IoT workload fluctuations, in addition to deciding on proper placement of the scaled instances on fogs with minimal cost on the fly. An effective scaling results in improving the response time and avoid service instability on fog devices. Therefore, we propose in this work FScaler, a reinforcement learning agent that horizontally scales container's instances after studying user's demands, and schedules the placement of newly created instances based on defined cost functions after studying the change in resources availability. The environment of FScaler is modeled as an MDP to be solved by any RL algorithm. For this work, we study the efficiency of our MDP formulation by solving the problem using SARSA. Promising results are shown through testing using a real-life dataset presenting the variation of user's demands of a particular service and the change in resource availability over time.

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19's communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users' profiles, meta-data and tweets' context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

With the increasing number of IoT devices, fog computing has emerged, providing processing resources at the edge for the tremendous amount of sensed data and IoT computation. The advantage of the fog gets eliminated if it is not present near IoT devices. Fogs nowadays are pre-configured in specific locations with pre-defined services, which limit their diverse availabilities and dynamic service update. In this paper, we address the aforementioned problem by benefiting from the containerization and micro-service technologies to build our on-demand fog framework with the help of the volunteering devices. Our approach overcomes the current limitations by providing available fog devices with the ability to have services deployed on the fly. Volunteering devices form a resource capacity for building the fog computing infrastructure. Moreover, our framework leverages intelligent container placement scheme that produces efficient volunteers' selection and distribution of services. An Evolutionary Memetic Algorithm (MA) is elaborated to solve our multi-objective container placement optimization problem. Real life and simulated experiments demonstrate various improvements over existing approaches interpreted by the relevance and efficiency of (1) forming volunteering fog devices near users with maximum time availability and shortest distance, and (2) deploying services on the fly on selected fogs with improved QoS.

Cloud computing has long been the main backbone that Internet of Things (IoT) devices rely on to accommodate their storage and analytical needs. However, the fact that cloud systems are often located quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. Given that fog nodes possess low resource capabilities compared to the cloud, matching the IoT services to appropriate fog nodes while guaranteeing minimal delay for IoT services and efficient resource utilization on fog nodes becomes quite challenging. In this context, the main limitation of existing approaches is addressing the scheduling problem from one side perspective, i.e., either fog nodes or IoT devices. To address this problem, we propose in this paper a multi-criteria intelligent IoT-Fog scheduling approach using game theory. Our solution consists of designing (1) preference functions for the IoT and fog layers to enable them to rank each other based on several criteria latency and resource utilization and (2) centralized and distributed intelligent scheduling algorithms that capitalize on matching theory and consider the preferences of both parties. Simulation results reveal that our solution outperforms the two common Min-Min and Max-Min scheduling approaches in terms of IoT services execution makespan and fog nodes resource consolidation efficiency.

Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

Distributed Denial of Service (DDoS) constitutes a major threat against cloud systems owing to the large financial losses it incurs. This motivated the security research community to investigate numerous detection techniques to limit such attack's effects. Yet, the existing solutions are still not mature enough to satisfy a cloud-dedicated detection system's requirements since they overlook the attacker's wily strategies that exploit the cloud's elastic and multi-tenant properties, and ignore the cloud system's resources constraints. Motivated by this fact, we propose a two-fold solution that allows, first, the hypervisor to establish credible trust relationships toward guest Virtual Machines (VMs) by considering objective and subjective trust sources and employing Bayesian inference to aggregate them. On top of the trust model, we design a trust-based maximin game between DDoS attackers trying to minimize the cloud system's detection and hypervisor trying to maximize this minimization under limited budget of resources. The game solution guides the hypervisor to determine the optimal detection load distribution among VMs in real-time that maximizes DDoS attacks' detection. Experimental results reveal that our solution maximizes attacks' detection, decreases false positives and negatives, and minimizes CPU, memory and bandwidth consumption during DDoS attacks compared to the existing detection load distribution techniques.

Fueled by changes in professional application models, personal interests and desires and technological advances in mobile devices, multi-persona has emerged recently to keep balance between different aspects, in our daily life, on a single mobile terminal. In this context, mobile virtualization technology has turned the corner and currently heading towards widespread adoption to realize multi-persona. Although recent lightweight virtualization techniques were able to maintain balance between security and scalability of personas, the limited CPU power and insufficient memory and battery capacities, still threaten personas performance and viability. Throughout the last few years, cloud computing has cultivated and refined the concept of outsourcing computing resources, and nowadays, in the coming age of smartphones and tablets, the prerequisites are met for importing cloud computing to support resource constrained mobiles. From these premises, we propose in this paper a novel offloading-based approach that based on global resource usage monitoring, generic and adaptable problem formulation and heuristic decision making, is capable of augmenting personas performance and viability on mobile terminals. The experiments show its capability of reducing the resource usage overhead and energy consumption of the applications running in each persona, accelerating their execution and improving their scalability, allowing better adoption of multi-persona solution.

While the usage of smart devices is increasing, security attacks and malware affecting such terminals are briskly evolving as well. Mobile security suites exist to defend devices against malware and other intrusions. However, they require extensive resources not continuously available on mobile terminals, hence affecting their relevance, efficiency and sustainability. In this paper, we address the aforementioned problem while taking into account the devices limited resources such as energy and CPU usage as well as the mobile connectivity and latency. In this context, we propose an ad-hoc mobile edge cloud that takes advantage of Wi-Fi Direct as means of achieving connectivity, sharing resources, and integrating security services among nearby mobile devices. The proposed scheme embeds a multi-objective resource-aware optimization model and genetic-based solution that provide smart offloading decision based on dynamic profiling of contextual and statistical data from the ad-hoc mobile edge cloud devices. The carried experiments illustrate the relevance and efficiency of exchanging security services while maintaining their sustainability with or without the availability of Internet connection. Moreover, the results provide optimal offloading decision and distribution of security services while significantly reducing energy consumption, execution time, and number of selected computational nodes without sacrificing security.

Intelligent Transportation System (ITS) help drivers by showing the shortest routes and some driving information such as congestion, accident and roadwork. Twitter traffic detection systems depend on real time collection of traffic and road-related data where users share real time events that can help extract traffic status on different roads. However, the current twitter-based approaches are not applied on Arabic traffic related tweets, and do not take into consideration tweets about roads that do not have congestion. In this paper, we address the aforementioned limitations by proposing a new proactive social learning approach for (1) detecting traffic related tweets, (2) extracting location using local dictionary and Google Maps API, (3) determining traffic status using Support Vector Machine (SVM), and (4) extracting the cause by classifying them into three different categories using incremental learning. Our experimental results show that our approach can identify tweets referring to traffic with an accuracy of 98%, determine jam status from those tweets by an accuracy of 91.1%, and identify the cause of traffic-related events with an accuracy of 84.7% per class label.

The prominence of cloud computing led to unprecedented proliferation in the number of web services deployed in cloud data centers. In parallel, service communities have gained recently increasing interest due to their ability to facilitate discovery, composition, and resource scaling in large-scale services' markets. The problem is that traditional community formation models may work well when all services reside in a single cloud but cannot support a multi-cloud environment. Particularly, these models overlook having malicious services that misbehave to illegally maximize their benefits and that arises from grouping together services owned by different providers. Besides, they rely on a centralized architecture whereby a central entity regulates the community formation; which contradicts with the distributed nature of cloud-based services. In this paper, we propose a three-fold solution that includes: trust establishment framework that is resilient to collusion attacks that occur to mislead trust results; bootstrapping mechanism that capitalizes on the endorsement concept in online social networks to assign initial trust values; and trust-based hedonic coalitional game that enables services to distributively form trustworthy multi-cloud communities. Experiments conducted on a real-life dataset demonstrate that our model minimizes the number of malicious services compared to three state-of-the-art cloud federations and service communities models.

In todays cloud market, providers are taking advantage of consumer reviews and ratings as a new marketing tool to establish their credibility. However, to achieve higher ratings, they need to enhance their service quality which comes with an additional cost. In this paper, we model this conflicting situation as a Stackelberg game between a typical service provider and multiple service users in a cloud environment. The strategy of the service provider is to adjust the price and IT capacity by predicting the users ratings as well as their demands variation in response to his given price, quality and rating. The game is solved through a backward induction procedure using Lagrange function and Kuhn-Tucker conditions. To evaluate the proposed model, we performed experiments on three real world service providers who have low, medium and high average of users' ratings, obtained from the Trust Feedback Dataset in the Cloud Armor project. The results show that improvement in ratings is mostly profitable for highly rated providers. The surprising point is that providers having low ratings do not get much benefit from increasing their average ratings, meanwhile, they can perform well when they lower the service price.

Insider attacks in which misbehaving Virtual Machines (VMs) take part of the cloud system and learn about its internal vulnerabilities constitute a major threat against cloud resources and infrastructure. This demands setting up continuous and comprehensive security arrangements to restrict the effects of such attacks. However, limited security resources prohibit full detection coverage on all VMs at all times, which can be exploited by attackers to examine the selective detection strategies and adjust their own attack plans accordingly. Motivated by the absence of any approach that accounts for such a challenge in the domain of cloud computing, we propose in this work an adaptive detection strategy that formulates a Stackelberg security game to enable the cloud system to optimally exploit its available amount of security resources to maximize the detection of distributed attacks, knowing that attackers have the ability to monitor the cloud system's strategies and adjust their own attack plans. Experiments carried out on the CloudSim framework reveal that the proposed solution maximizes the detection of distributed attacks and minimizes false negatives and positives compared to a maximin-based detection strategy, while being scalable to the increase in both the number of co-hosted VMs and percentage of co-resident attackers.

As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving as well. Security risks affecting the confidentiality, integrity, and privacy of smart devices are rapidly emerging. Mobile security suites exist to defend the device against malware and other intrusions. However, they require extensive resources which is a constraint of the device itself. In this paper, we address the problem of intrusion detection for smart devices taking into account the devices' limited resources such as energy, CPU usage, and internet connectivity. We provide an ad-hoc mobile cloud based intrusion detection framework that takes advantage of Wi-Fi Direct as means of achieving connectivity and sharing resources and services for providing security. The proposed framework allows exchange of data with or without the availability of internet connection. The paper also provides experiments, carried out using real devices, showing various improvements of using our approach. Up to 61% and 40% enhancement in energy consumption and response time respectively is reached compared to local execution.

Mobile virtualization is a key technology that is witnessing widespread adoption to realize multi-persona functionality capable of accommodating work, personal, and mobility needs on a single mobile terminal. Yet, unlike virtualization on servers and desktop machines, mobile virtualization is more challenging due to the limited resources on mobiles platforms in terms of CPU, memory and battery. The evolution of mobile virtualization ranged from heavy to more lightweight techniques capable of running virtual environments on mobile devices with lower overhead. Even though the latest proposed lightweight approaches were able to realize multi-persona, yet none of them is capable of efficiently managing personas performance or ensuring their viability. In parallel, to address the resource limitations of mobile platforms, many researchers have proposed offloading techniques to migrate computation intensive components out of the mobile device to be executed on resourceful mobile cloud computing infrastructure. Motivated by their promising results, we propose in this paper the integration of offloading in the virtual environments on the mobile device toward augmenting personas performance and ensuring their viability. Our experiments show very promising results in this regard.

This paper looks into ways of supporting social Web services react to the behaviors that their peers expose at run time. Examples of behaviors include selfishness and unfairness. These reactions are associated with actions packaged into capabilities. A capability allows a social Web service to stop exchanging private details with a peer and/or to suspend collaborating with another peer, for example. The analysis of capability results into three types referred to as functional (what a social Web service does), non-functional (how a social Web service runs), and social (how a social Web service reacts to peers). To avoid cross-cutting concerns among these capabilities aspect-oriented programming is used for implementing a system.

Policy-based computing is taking an increasing role in providing real-time decisions and governing the systematic interaction among distributed cloud and Web services. XACML has been known as the de facto standard widely used by many vendors for specifying access control and context-aware policies. Accordingly, the size and complexity of XACML policies are significantly growing to cope with the evolution of web-based applications. This growth raised many concerns related to the efficiency of real-time decision process (i.e. policy evaluation). This paper is addressing this concern through the elaboration of SBA-XACML, a novel set-based algebra scheme that provides efficient evaluation of XACML policies. Our approach constitutes of elaborating (1) set-based language that covers all the XACML components and establish an intermediate layer to which policies are automatically converted, and (2) policy evaluation module that provides better performance compared to the industrial standard Sun Policy Decision Point (PDP) and its corresponding ameliorations. Experiments have been conducted on real-life and synthetic XACML policies in order to demonstrate the efficiency, relevance and scalability of our proposition. The experimental results explore that SBA-XACML evaluation of large and small sizes policies offers better performance than the current approaches, by a factor ranging between 2.4 and 15 times faster depending on policy size.

In this paper, we detect two attacks targeting the QoS-OLSR protocol MANET. The Cluster-based model QoS-OLSR is a multimedia protocol designed on top of Optimized Link State Routing (OLSR) protocol. The quality of service (QoS) of the nodes is considered during the selection of the multi-point relays (MPRs) nodes. In this work, we identify two attacks that can be launched against the QoS-OLSR protocol: Identity spoofing attack, and wormhole attack. Watchdogs are used to detect the attacks performed by malicious nodes. As a solution, we propose to improve the watchdogs' detection by (1) using cooperative watchdog model and (2) adding the posterior belief function using Bayes' rule to the watchdog model. Simulation results show that the use of the Bayes' rule function along with the cooperative watchdog model improves the detection rate and reduces the false positives.

By handling the orchestration, composition and interaction of Web services, the Business Process Execution Language (BPEL) has gained tremendous interest. However, such process-based language does not assure a secure environment for Web services composition. The key solution cannot be seen as a simple embed of security properties in the source code of the business logic since the dynamism of the BPEL process will be affected when the security measures get updated. In this context, several approaches have emerged to tackle such issue by offering the ability to specify the security properties independently from the business logic based on policy languages. Nevertheless, these languages are complex, verbose and require programming expertise. Owing to these difficulties, specifying and the enforcing BPEL security policies become very tedious tasks. To mitigate these challenges, we propose in this paper, a novel approach that takes advantage of both the Unified Modeling Language (UML) and the Aspect Oriented Paradigm (AOP). By elaborating a UML extension mechanism, called UML Profile, our approach provides the users with model-based capabilities to specify aspects that enforce the required security policies. On the other hand, it offers a high level of flexibility when enforcing security hardening solutions in the BPEL process by exploiting the AOP approach. We illustrate our approach through an example of the dynamic generation and integration of model-based security aspects in a BPEL process.

The QOLSR is a multimedia protocol that was designed on top of the Optimized Link State Routing (OLSR) protocol. It considers the Quality of Service (QoS) of the nodes during the selection of the Multi-Point Relay (MPRs) nodes. One of the drawbacks of this protocol is the network lifetime, where nodes with high bandwidth but limited energy can be selected to serve as MPRs. This would drain the nodes' residual energy and shorten the network lifetime. In this paper, we consider the tradeoff between prolonging the ad hoc network lifetime and QoS assurance based on QOLSR routing protocol. This can be achieved by (1) reducing the number of Multi-Point Relay (MPR) nodes without sacrificing the QoS and (2) considering the residual energy level, connectivity index, and bandwidth of these relay nodes. These objectives can be reached by deploying the clustering concept to QOLSR. Therefore, we propose a novel clustering algorithm and a relay node selection based on different combinations of metrics, such as connectivity, residual energy, and bandwidth. Four cluster-based models are derived. Simulation results show that the novel cluster-based QoS-OLSR model, based on energy and bandwidth metrics, can efficiently prolong the network lifetime, ensure QoS and decrease delay.

We propose in this paper a new approach for the dynamic enforcement of Web services security, which is based on a synergy between Aspect-Oriented Programming (AOP) and composition of Web services. Security policies are specified as aspects. The elaborated aspects are then weaved (integrated) in the Business Process Execution Language (BPEL) process at runtime. The main contributions of our approach are threefold: (1) separating the business and security concerns of composite Web services, and hence developing them separately (2) allowing the modification of the Web service composition at run time and (3) providing modularity for modeling cross-cutting concerns between Web services. We demonstrate the feasibility of our approach by developing a Flight System (FS) that is composed of several Web services. First, a RBAC (Role Based Access Control) model for the flight system, which we called RBAC-FS, is elaborated. Afterwards, the Web services that implement the security features are developed. Finally, the BPEL aspects that integrate the security functionalities dynamically into the BPEL process are created. The devised aspects realize the elaborated RBAC-FS model and provide authentication and access control features to the flight system. Case studies and experimental results are also presented to defend our propositions.

Security is a very challenging task in software engineering. Enforcing security policies should be taken care of during the early phases of the software development life cycle to prevent security breaches in the final product. Since security is a crosscutting concern that pervades the entire software, integrating security solutions at the software design level may result in scattering and tangling security features throughout the entire design. To address this issue, we propose in this paper an aspect-oriented approach for specifying and enforcing security hardening solutions. This approach provides software designers with UML-based capabilities to perform security hardening in a clear and organized way, at the UML design level, without the need to be security experts. We also present the SHP profile, a UML-based security hardening language to describe and specify security hardening solutions at the UML design level. Finally, we explore the efficiency and the relevance of our approach by applying it to a real world case study and present the experimental results.

In this paper, we propose an approach for systematic security hardening of software based on aspect-oriented programming and Gimple language. We also present the first steps towards a formal specification for Gimple weaving together with the implementation methodology of the proposed weaving semantics. The primary contribution of this approach is providing the software architects with the capabilities to perform systematic security hardening by applying well-defined solutions and without the need to have expertise in the security solution domain. We explore the viability of our propositions by realizing the weaving semantics for Gimple by implementing it into the GCC compiler and applying our methodologies for systematic security hardening to develop a case study for securing the connections of client applications together with experimental results.

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