Autonomous surface ships improve urban transport by boosting efficiency, reducing congestion, and enhancing sustainability. These ships rely on the Automatic Identification System (AIS) for real-time tracking, transmitting essential data such as location, velocity, and direction. However, AIS messages are vulnerable to cyber threats and signal tampering due to the lack of cryptographic authentication. Attackers can compromise transmissions, leading to navigational errors and disrupted operations. Ensuring secure AIS message exchange is critical for the safe and efficient monitoring of autonomous ships (AS). This work introduces a Proximal Policy Optimization (PPO)-enhanced Anomaly Detection Mechanism (ADM) to identify AIS spoofing attacks. The proposed system continuously monitors AIS streams, cross-validates positions from surrounding ships, and uses PPO to detect irregularities in navigation patterns. By analyzing historical traffic behaviors, the ADM effectively differentiates legitimate deviations from manipulated data. Upon detecting anomalous activity, the system autonomously flags threats. Experimental findings reveal that PPO converges rapidly and surpasses traditional sequence-based models, consistently achieving over 96% detection accuracy with minimal false positives. Furthermore, trajectory anomaly classification analysis underscores PPO’s flexibility, establishing it as an optimal solution for real-time anomaly detection in next-generation urban water transport networks.

The advancement of Intelligent Transport Systems (ITS) and the Internet of Vehicles (IoV) enhances road safety and traveler comfort by enabling safer, more efficient transportation networks. However, these technologies are vulnerable to a range of security threats that malicious actors could exploit. One of the most severe threats to IoV is the Distributed Denial of Service (DDoS) attack, which could disrupt traffic flow, disable vehicular communication, or even cause accidents. This paper proposes a novel Deep Multimodal Learning (DML) approach to detect DDoS attacks in IoV, strengthening cybersecurity in intelligent transport systems. Our DML model integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, enhanced by Attention and Gating mechanisms, alongside a Multi-Layer Perceptron (MLP) with a multimodal intermediate fusion architecture. This innovative method leverages the Framework for Misbehavior Detection (F2MD) to generate a synthetic dataset and deploy our model, enabling real-time DDoS detection and mitigation while overcoming the limitations of the Vehicular Reference Misbehavior (VeReMi) dataset. The proposed approach is evaluated in real-time across various simulated real-world scenarios with differing attacker densities. Our DML model achieves an average accuracy of$\mathbf{9 6. 6 3 \%}$, outperforming classical Machine Learning (ML) and state-of-the-art approaches, demonstrating significant efficacy and reliability in safeguarding vehicular networks against malicious cyberattacks.

Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats.

The widespread adoption of Electric Vehicles (EVs) requires an efficient charging infrastructure. Using data connection-based charging equipment, smart charging, and vehicle-to-grid (V2G) charging technologies allow electric vehicles to connect to the electrical grid. This enables the exchange of information and instructions. However, this ecosystem is susceptible to physical or cyberattacks, just like any other cyber-physical system. In this paper, we investigate load-altering (LA) attacks that impact the functionality of the smart grid. We propose a two-phase strategy to avoid, detect, and mitigate attacks. Our first step is scheduling charging station operations to normalize system utilization to avert future simultaneous attacks. Therefore, a distributed Deep Reinforcement Learning (DRL) model is used to establish the ON/OFF status of each charging station. To maintain the system's power stability during the charging process, the system uses an event-based method to respond to the abrupt change in charging/discharging behavior. To detect attacks, a second multi-agent deep-reinforcement learning model is created. This enables the system to recognize and neutralize the effects of LA attacks on the power grid. After the compromised entities are located, they are isolated and the demands of certain backup charging stations either completely or partially replace the canceled power demands. The performance study shows that the suggested strategy minimizes the impact of LA attacks and delivers good results in terms of detection accuracy.

Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks.

With the widespread adoption of Electrical Vehicles (EVs), EV charging infrastructure has become more advanced. Unfortunately, this cyber-physical system is vulnerable to physical or cyberattacks. In this work, we focus on load-altering (LA) attacks that affect the operation of the power grid. We propose a distributed multi-agent Deep Reinforcement Learning (DRL) based solution to detect, identify, and mitigate LA attacks. The defense system responds to the sudden change in charging/discharging behavior using an event-based approach to preserve power stability throughout the charging process. The proposed approach allows the system to identify and counteract the impact of LA attacks on the electrical grid. After the compromised entities are identified, they are isolated, and a backup charging strategy is applied to fully or partially recover the operation of the charging system. According to the performance evaluation, the proposal reduces the effect of LA attacks while producing good detection accuracy rates.

O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN’s highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments.

The Open Radio Access Network (O-RAN) architecture has enabled the integration of multi-vendor equipment, yielding a significant enhancement in the flexibility and interoperability of telecommunications networks. However, this openness has also introduced new security vulnerabilities, particularly in supply chain integrity. Malicious actors may exploit weaknesses at various stages of production, distribution, or integration, leading to critical threats such as data tampering, unauthorized access, and denial-of-service (DOS) attacks. To address these challenges, this paper proposes a novel blockchain-based framework designed to secure the O-RAN supply chain. The proposed solution leverages a private permissioned blockchain ledger and cryptographic firmware authentication to ensure the integrity and authenticity of network equipment throughout its lifecycle. Specifically, the framework consists of: (1) a decentralized architecture integrating blockchain network components, equipment node validators, and secure firmware authentication mechanisms; and (2) a consensus-based verification model to enhance trust and transparency within the supply chain. To the best of our knowledge, this is one of the first approaches to use blockchain for O-RAN supply chain security, and also addressing emerging security threats in a scalable and tamper-resistant manner. Experimental validation and security assessments demonstrate the effectiveness of the proposed framework in mitigating supply chain risks, making it a promising solution for ensuring trust and robustness in next-generation O-RAN ecosystems.

The advent of the QUIC protocol may herald a significant shift in the composition of online traffic in the years to come. The transport layer encryption of the QUIC protocol is one of its main evolutions, especially for metadata that was previously transmitted over TCP traffic without encryption. This new protocol has the potential to require significant alterations in future Internet traffic analysis methods and impact network intrusion detection. On the other side, Machine learning has been used in several research projects to identify network intrusions, with positive outcomes. However, we must take into account new evolution of network traffic. In this paper, we propose a new approach that employs supervised machine learning algorithms to identify flows generated by bots interacting with a Web server during a DDoS attack, focusing on the challenges posed by the QUIC protocol and its implications for effective intrusion detection and cybersecurity. Our contribution in this work is divided into three main parts: 1) A guided process with model architecture for emulating and collecting traffic that depict a range of situations our system may encounter; 2) an analysis module that consists on the creation of two labeled datasets, where observations represent the traffic flows detected in PCAP files. We studied the relevance of different features for these datasets, contributing to a thorough understanding of the quality of the data used; 3) a real world experimention for evaluating the effectiveness of several supervised machine learning algorithms on our datasets. This experimentation allows us to determine which algorithm provides the best prediction results.

The implication of cyber-physical systems into industrial processes has introduced some security breaches due to the lack of security mechanisms. This article aims to come up with a novel methodology to detect false data injection attacks on cyber-physical systems. To reach this goal, we propose an efficient anomaly-based approach for detecting false data injection attacks against industrial cyber-physical systems. Particularly, we use sequential pattern mining techniques, which are commonly used for learning most important patterns of a system. In our case, the frequent pattern learning algorithm is used to create a database corresponding to the normal operation of the system, then, this database is fed into an attack detection algorithm in order to alert the user whenever an attack is occurring. The extensive simulations prove that our attack detection approach is able to detect attacks with a great accuracy and that this methodology could work even for large scale systems.

Intelligent Transportation Systems (ITSs) are transforming the global monitoring of road safety. These systems, including vehicular networks and transportation infrastructure, are vulnerable to several security issues, which could disrupt services and potentially cause harm to the users. It is crucial to establish robust security measures to protect against evolving attacks and ensure the safe and reliable operation of ITS. Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) are mainly used to enhance the security of ITS. The adoption of AI-based techniques to secure ITS against new emerging threats has been limited due to a lack of realistic and recent data on these types of attacks ($i.e.,$zero-day attacks). In this context, we introduce a novel Edge-based Framework that uses Federated Learning (FL) and blockchain to secure ITS against new emerging threats. In particular, our proposed framework consists of (1) a novel distributed Edge-based architecture that allows multiple Edge nodes to securely collaborate while preserving their privacy; and (2) a decentralized and secure reputation system based on blockchain technology to maintain the reliability and trustworthiness of the FL process within the ITS; This system manages reputation data for individual nodes (such as vehicles), guaranteeing the integrity of the FL training process. Experiment results using the UNSW-NB15 dataset show that our proposed framework achieves high accuracy and F1 score (99%) in detecting new threats while ensuring the privacy and reliability of the whole ITS. These results demonstrate the effectiveness of our proposed framework in securing ITS.

The rapid expansion of the Internet of Things (IoT) has increased the demand for robust intrusion detection systems. Federated learning (FL) has appeared as a potential solution to improve the security of IoT networks by facilitating collaboration between multiple devices in training a unified model while keeping their data secure. As the training process of FL occurs locally on individual devices, preserving data privacy becomes crucial. Additionally, combining model updates from multiple devices into a unified model can be challenging. Therefore, addressing these issues is critical to effective and private FL-based intrusion detection in IoT networks. In this paper, we propose a novel approach for ensuring privacy and efficiency in FL for robust intrusion detection in the IoT. Our approach combines secure aggregation and blockchain technology to protect the privacy of IoT data while enabling efficient and accurate model training. We first introduce a secure aggregation algorithm that can be used to combine the model updates from multiple devices in a privacy-preserving manner. This algorithm uses multi-party computation to prevent any single party from seeing the data of the other parties, thereby ensuring that the privacy of IoT data is maintained throughout the model training process. Then, we incorporate the use of blockchain technology to ensure data integrity and prevent tampering. Finally, we perform experiments on real-world IoT datasets to demonstrate the effectiveness of our approach. Our results show that our approach achieves high accuracy in intrusion detection while preserving the privacy of the IoT data.

Access control, both physical and virtual, has always been a crucial aspect in maintaining the security of corporate information systems. Recently, several solutions have been proposed to address physical and virtual access control, including the use of electronic badges that can be costly to produce and easily misplaced. Additionally, numerous, sometimes expensive, cloud-based solutions have also been adopted. However, these solutions are often provided by third-party organizations, which require entities to place trust in these providers, a risk that is unacceptable for industries such as the military and banking. To solve this issue, we propose a novel Blockchain-based solution to establish a scalable and secure system for managing access controls. Blockchain offers a secure, decentralized, and most importantly, immutable alternative that eliminates the need for trust in third-party providers. We have implemented, tested, and deployed our Blockchain-based access control architecture on the Avalanche official network. The results demonstrate that this solution offers strong security, flexibility, efficiency, and cost-effectiveness, making it a promising approach to mitigate Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT). Our deployment on the Avalanche network confirms the feasibility and robustness of our approach in a real-world setting.

In a time where data traffic is booming, Software-defined Networking (SDN) is becoming the most plausible solution to cope with the conventional networks’ shortcomings. The separation of the control (software) and the data (hardware) planes makes of SDN a technology-enabled to a multitude of new and promising applications and use cases for 5G network technology. Yet, initial SDN deployments considered having a single controller for the entire network. However, this arrangement is deemed to be counterproductive in large-scale and ultra-reliable networks based SDN as the controller might become the system's bottleneck. To address this issue, several multi-controller architectures were proposed. They are of three types: flat, hierarchical or hybrid. For theses architectures to be potent for 5G core networks, efficient path computation and flow tables update mechanisms are needed. In this paper, we propose HybCon, a hybrid SDN-based distributed Cloud architecture having the control plane distributed over different controller types (i.e., Fog, Edge, SDN). When a path computation request is received, HybCon involves some/all of the controllers and uses a multi-priority queueing model along with node parallelism to expedite the path computation and to cut down the synchronization overhead. Simulation results show that HybCon outperforms existing schemes in terms of path computation time, path setup latency, packet delivery ratio and communication overhead, making it a perfect solution for future 5G networks.

Several works are aiming to develop techniques allowing detecting False Data Injection Attacks, which represents one of the most harmful attacks due to its ability to damage a Cyber Physical Systems (CPS). Among these techniques the watermarking represents one of the most used ones. This paper proposes the design of a False Data Injection Attack (FDIA) against a CPS protected by a watermark-based detector. The attack herein proposed is achieved in two phases. The first one is a passive phase, where the adversary builds a black box model of the system. Then, he uses the already built model to create the FDIA without being detected by the watermark based detector. The extensive simulations prove that this attack could be used to deceive the system even with the presence of a dynamic watermark.

In recent years, there has been a huge demand to secure Internet of Things (IoT) applications against the new emerging threats and attacks; these attacks are becoming increasingly sophisticated and have caused tremendous damage to academic and business organizations. Intrusion detection systems (IDS) have an essential role in ensuring network security. As new types of security threats emerge, conventional IDSs that rely on pattern matching are constrained by their need for new attack patterns. To address this problem, machine learning and deep learning (ML/DL) techniques have been proposed in the literature to improve the detection capability of traditional IDS. In this paper, we study a new problem of using a lightweight adaptive boosting technique (the AdaBoost algorithm) for intrusion detection in software defined networks (SDNs). In particular, we propose a hierarchical Fog Computing Framework, called ML-FoG, that uses both advanced ML techniques with a new feature selection scheme to efficiently detect security threats in SDNs; ML-FoG consists of: (1) a Network data Flow Collection module (NFC) that gathers network features in a scalable way; (2) a Gradient Boosting Feature Selection Module (GBSM) that selects the most informative and relevant features; and (3) a novel lightweight adaptive boosting scheme that uses AdaBoost to detect network security threats in a timely and effective manner. Experimental results using UNSW-NB15 demonstrate that ML-FoG outperforms state-of-the-art contributions in accuracy and detection rate, while greatly decreasing the computational complexity.

Vehicle Ad-hoc Network (VANET) is the main component of the intelligent transportation system. With the development of the next-generation intelligent vehicular networks, the latter aims to provide strategic and secure services and communications in roads and smart cities. Due to VANET’s unique characteristics, such as high mobility of its nodes, self-organization, distributed network, and frequently changing topology, security, data integrity, and users’ privacy information are major concerns. Also, attack prevention is still an open issue. Distributed Denial of Service (DDoS) is one of the most dangerous attacks in VANETs, which aims to flood the system’s bandwidth. In this article, we propose a hierarchical architecture for securing Software-Defined Vehicular Network (SDVN) and a security model for predicting and detecting DDoS attacks based on behavioral analysis of nodes achieved by a Markov stochastic process. Simulation results show that our model effectively mitigates DDoS attacks with a high-reliability rate.

Software-defined network (SDN) is widely deployed on Smart Grid (SG) systems. It consists in decoupling control and data planes, to automate the monitoring and management of the communication network, and thus enabling zero touch management of SG systems. However, SDN-based SG is prone to several security threats and varios type of new attacks. To alleviate these issues, various Machine/Deep learning (ML/DL)-based intrusion detection systems (IDS) were designed to improve the detection accuracy of conventional IDS. However, they suffer from high variance and/or bias, which may lead to an inaccurate security threat detection. In this context, ensemble learning is an emerging ML technique that aims at combining several ML models; the objective is to generate less data-sensitive (i.e., less variance) and more flexible (i.e., less bias) machine learning models. In this paper, we design a novel framework, called BoostIDS, that leverages ensemble learning to efficiently detect and mitigate security threats in SDN-based SG system. BoostIDS comprises two main modules: (1) A data monitoring and feature selection module that makes use of an efficient Boosting Feature Selection Algorithm to select the best/relevant SG-based features; and (2) An ensemble learning-based threats detection moel that implements a Lightweight Boosting Algorithm (LBA) to timely and effectively detects SG-based attacks in a SDN environment. We conduct extensive experiments to validate BoostIDS on top of multiple real attacks; the obtained results using NSL-KDD and UNSW-NB15 datasets, confirm that BoostIDS can effectively detect/mitigate security threats in SDN-based SG systems, while optimizing training/test time complexity.

Attacks against the IoT network are increasing rapidly, leading to an exponential growth in the number of unsecured IoT devices. Existing security mechanisms are facing several issues due to the lack of real-time decisions, high energy consumption, and high time delays. In this context, we propose a novel Low-Latency Fog-based Framework, called FogFed, to secure IoT applications using Fog computing and Federated Learning (FL). The fog brings security mechanisms near IoT devices reducing delays in communication, while FL enables a privacy-aware collaborative learning between IoT while preserving their privacy. FogFed combines two levels of detection, Fog-based IoT attack detection using a binary FL classifier and cloud-based IoT attack detection using a Multiclass FL classifier. The in-depth experiments results with well-known IoT attack/malware using, the UNSW-NB15 datastet, show the significant accuracy (99%) and detection rate (99%), which outperforms centralized ML/DL models, while significantly reducing delays and preserving the privacy.

The vehicle adhoc network (VANET) is a promising technology that enables numerous vehicular network applications to improve road safety, navigation, and many other purposes. Android automotive supports many applications for vehicles. Each application has a list of accesses, called permissions, required for specific interfaces or sensitive data. However, some applications request permissions unrelated to functionalities or unnecessary permissions. Furthermore, they improperly collect user data. Given the privacy risk associated with applications, it is necessary to study the permissions requested by the application before installation. A permission system is a solution to deal with abusive applications. However, such a system suffers from limitations as users may ignore it during the installation phase due to the complexity of understanding the permissions. This article proposes a graph-based model to determine abusive applications by automatically analyzing the requested permissions. This aims to build a confidence indicator to choose the applications with more respect for privacy. This model would inform the user about the possibility of data leakage risks by assigning a privacy score.

Industry 5.0 is rapidly growing as the next industrial evolution, aiming to improve production efficiency in the 21stcentury. This evolution relies mainly on advanced digital technologies, including Industrial Internet of Things (IIoT), by deploying multiple IIoT devices within industrial systems. Such a setup increases the possibility of threats, especially with the emergence of IIoT botnets. This can provide attackers with more sophisticated tools to conduct devastating IIoT attacks. Besides, machine learning (ML) and deep learning (DL) are considered as powerful techniques to efficiently detect IIoT attacks. However, the centralized way in building learning models and the lack of up-to-date datasets that contain the main attacks are still ongoing challenges. In this context, multiaccess edge computing (MEC) and federated learning (FL) are two promising complementary technologies. MEC brings computing capabilities at the edge of the industrial systems, while FL leverages the edge resources to enable a privacy-aware collaborative learning, especially in multiindustrial systems context. In this article, we design a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame. Specifically, FedGame enables multiple MEC domains to collaborate securely to deal with an IIoT attack, while preserving the privacy of IIoT devices. Moreover, a noncooperative game is formulated on the top of FedGame, to enable MEC nodes acquiring the needed virtual resources from the centralized MEC orchestrator, to deal with each type of IIoT attacks. We evaluate FedGame using real-world IIoT attacks; the experimental results show not only the accuracy of FedGame against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing with IIoT attacks.

Recently, software defined networks (SDN) has emerged as novel technology that leverages network programmability to facilitate network management. SDN provides a global view of the network, through a logically centralized component, called SDN controller, to strengthen network security. SDN separates the control plane from the data plane, which allows for a more control over the network and brings new capabilities to cope with the new emerging security threats (i.e., zero-day attacks). Existing attack detection schemes are facing obstacles due to high false positive rates, low detection performances, and high computational costs. To address these issues, we propose a multi-module Machine Learning (ML) framework that combines unsupervised ML techniques with a scalable feature collection and selection scheme to effectively/timely detect network security threats in the context of SDN. In particular, our proposed framework consists of: (1) a data flow collection module (DFC) to gather the features of network data in a scalable and efficient way using sFlow protocol; (2) an Information gain Feature Selection (IGF) module to select the most informative/relevant features to reduce training and testing time complexity; and (3) a novel unsupervised ML module that uses a novel outlier detection scheme, called Isolation Forest (ML-IF), to effectively/timely detect network security threats in SDN. The experimental results using the well-known public network security dataset UNSW-NB15, show that our proposed framework outperforms state-of-the-art contributions in terms of accuracy and detection rate while significantly reducing computational complexity; making it a promising framework to mitigate the new emerging network security threats in SDN.

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB- FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.

Wireless Sensor/Actuator Networks (WSANs) enable prominent services in different domains including smart cities, industry, and agriculture. These services may demand high quality of service (QoS) requirements in terms of data rate, throughput, latency, which can be challenging regarding the specific characteristics of such networks (e.g., a huge number of connected devices, the communication mode, limited resources, etc.). In this paper, we focus on the transmission power allocation problem to match the QoS requirements in terms of data rate maximization and interference minimization. We propose a game theory-based channel selection and transmission power determination scheme in WSANs. As a first step, we formulate the problem as a constrained multi-objective optimization problem. This problem is a tradeoff between the maximization of the data rate of each node and the minimization of the transmission power to reduce the interference ratio. The second step consists of the proposition of a multi-leader-follower game model to determine the transmission channel and power with consideration of data rate, packet deadlines, and fairness between nodes. Finally, we perform extensive simulations to evaluate the proposed scheme performance.

The emergence of Internet of Energy (IoE) paves the way for sustainable and green energy environments that reduce energy costs and integrate Renewable Energy Sources (RESs) as new sources of energy. Electric vehicles (EVs) are one of the main actors of IoE future. The emergence of EVs promises to reduce the environmental crisis (e.g., carbon emissions); however, their charging process will consume massive amounts of electricity and may affect the reliability of the Smart Grid (SG). Recently, vehicle-to-vehicle (V2V) electricity trading approach has gained momentum as a novel strategy that reduces the peak power consumption in SG. In this context, EVs compete to provide electricity with lower prices, while maintaining the V2V electricity trading system secure. However, they lack flexibility, transparency, and authenticity. More importantly, they are based on centralized models (i.e., EV aggregators) which introduce single-point-of-failure and may cause the collapse of the system. In this paper, we propose a fully decentralized blockchain-based system that allows for an automated, fair, and trustworthy V2V electricity trading system; it uses Ethereum’s smart contracts to realize the V2V electricity trading system in a fully distributed, transparent, secure, tamper-proof and trustworthy manner. The proposed system is implemented, tested, and deployed on the Ethereum official test network Ropsten. The experiment results show that the proposed solution achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to new decentralized V2V electricity trading systems in SG.

Recently, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized applications (e.g., Internet of Things (IoT)). However, its integration with the IoT is still facing several problems (e.g., scalability, flexibility). Provisioning resources to enable a large number of connected IoT devices implies having a scalable and flexible blockchain. To address these issues, we propose a scalable and trustworthy blockchain (STB) architecture that is suitable for the IoT; which uses blockchain sharding and oracles to establish trust among unreliable IoT devices in a fully distributed and trustworthy manner. In particular, we design a Peer-To-Peer oracle network that ensures data reliability, scalability, flexibility, and trustworthiness. Furthermore, we introduce a new lightweight consensus algorithm that scales the blockchain dramatically while ensuring the interoperability among participants of the blockchain. The results show that our proposed STB architecture achieves flexibility, efficiency, and scalability making it a promising solution that is suitable for the IoT context.

Vehicular Ad hoc Network (VANET) is a wireless technology dedicated to vehicular communications. VANET is unsecure because of its lack of central administration and is also vulnerable due to its unique characteristics. Therefore, security is one of the most critical issues in this type of network. For a high level of safety, we need a method to evaluate vehicular communications’ trust and measure nodes’ reliability. This article presents a decentralized reputation model, where the activity of each node (i.e., vehicle) is observable for adjacent nodes. Our model enables nodes to detect malicious vehicles and avoids interacting with them. We propose to use the Bayesian filter to measure the trust scores of vehicles accurately. Different from conventional trust models, we study the concept of classification and misclassification. We then analyze the accuracy of our Bayesian filter by computing the factors of Node Recall and Node Precision. The extensive simulations have shown that the proposed filter can assign an accurate trust score to nodes under various network conditions and misbehavior rates.

In the recent decades, Intelligent Transport Systems (ITS) attracted researchers’ great attention. ITS plays a very important role in making citizens’ lives easier in term of mobility, safety, quality of life and security. Vehicular ad-hoc networks (VANETs) became an inseparable component of ITS. The current architecture has been facing many issues due to VANET’s characteristics such as high mobility of its nodes and it is still vulnerable to important security attacks which threatens its main security services such as availability, data integrity, authentication and privacy. We propose a new VANET architecture called FCSDVN-ML, in which we combine three emerging paradigms: Software-Defined Network (SDN), Fog Computing (FC) and Machine Learning (ML) to improve security in VANETs. In this paper, we described our architecture components and we discussed its potential performance against Distributed Denial of Service (DDoS) attack using the hierarchical firewalls.

In this paper, we propose a multidimensional trust model for vehicular networks. Our model evaluates the trustworthiness of each vehicle using two main modes: 1) Direct Trust Computation DTC related to a direct connection between source and target nodes, 2) Indirect Trust Computation ITC related to indirectly communication between source and target nodes. The principal characteristics of this model are flexibility and high fault tolerance, thanks to an automatic trust scores assessment. In our extensive simulations, we use Total Cost Rate to affirm the performance of the proposed trust model.

Nowadays, blockchain technology is seen as one of the main technological innovations to emerge since the advent of the internet. Many applications can benefit from blockchain to protect their exchanges. Nonetheless, applications with more restricted interests cannot use public blockchains. Permissioned blockchains promise to combine effectiveness of blockchains with stricter permissions to join blockchain's network. In permissioned blockchain, the number of participating entities is limited compared to public blockchain. However, by targeting the peers of the blockchain, the attackers can easily take control of consensus process and halt the blockchain operations. In this paper, we propose BrainChain, a scalable and efficient scheme to protect permissioned blockchain nodes from the largest ever Distributed Denial of Service (DDoS) attack (i.e., Domain Name System (DNS) amplification attack) in the context of software defined networks (SDN). BrainChain consists of 4 schemes: (1) Flow statistics collection scheme (FS) to gather the features of flows in an efficient way using sFlow; (2) Entropy based scheme (ES) to measure disorder of network features; (3) Bayes Network based Filtering scheme (BF) to classify, based on entropy values, illegitimate DNS requests; and (4) DNS Mitigation (DM) scheme to mitigate in an effective way the illegitimate flows (i.e., illegitimate DNS requests). Experimental results show that BrainChain can quickly and effectively detect and mitigate the attacks (i.e., DNS amplification attacks) with a high accuracy and a small false positive rate making it a promising scheme to protect blockchain applications from DNS Amplification attacks.

Smart grids (SGs) and advanced metering infrastructures (AMIs) are considered as the new evolution of classical electrical grids. The recent emergence of smart meters is paving the way for the proliferation of smart grids, where billions of smart meters are interconnected to provide novel pervasive services (e.g., real time pricing application and real time energy consumption), and automate diagnostic and daily energy metering (i.e., gas, electric) tasks (e.g., billing, monitoring, planning and predicting of energy usage). The recent explosion in the number of insecure smart meters is changing the view towards SG from enabler of smart homes into a powerful amplifying tool that creates new vectors for cyberattacks (i.e., smart-homes Distributed Denial-of-Service (DDoS) attacks) at large scale. This motivated us to design a new flexible, secure, efficient and trustworthy access control scheme based on blockchain and smart contract. Although access control exists in AMI, it is based on a centralized model (i.e., router/gateway, firewall) which introduces a bottleneck (i.e., single point of failure) and causes the collapse of the system. In this paper, we propose a new decentralized-based access control architecture for SG based on blockchain; it uses smart contracts (i.e., Ethereum's smart contracts) in order to manage permissions in a fully distributed and trustworthy manner. The architecture is implemented, tested and deployed on the Ethereum official test network Ropsten [1]. The results confirm that the proposed blockchain based access control scheme achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to mitigate DDoS attacks in SGs.

In this paper, we investigate the concept of reputation to improve the resistance of vehicular networks against malicious and misbehaving vehicles. We propose a robust reputation management system, which consists of a model for reputation calculation and a credibility model to enhance network efficiency. The reputation score or value reflects the behavior of a vehicle towards other vehicles and network services (selfish, cooperative, malicious, misbehaving, etc.); while the credibility of vehicles is used to determine whether a reputation score given by a vehicle is correct to deal with malicious vehicles that use reputation calculation to spoil the network operation. We first describe how the reputation score of each vehicle is determined. Then, we introduce a non-cooperative game, where each vehicle aims to maximize its credibility. The effectiveness of the proposed model is demonstrated through extensive simulations.

The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.

Electric vehicles (EVs) are likely to become very popular within the next few years. With possibly millions of such vehicles operating across the smart cities, smart grid energy providers can be directly impacted by the charging of EV batteries. In order to reduce this impact and optimize energy saving, in this article, we propose a coordinated model for scheduling the plug-in of EVs for charging and discharging energy. The model is based on a new decentralized Fog architecture for smart grid in order to reduce the completion and communication delay of EV energy demand scheduling. To enhance the scheduling of EV demands and predict the future energy flows, we propose a plug-in system of EVs based on calendar planning. We develop a mathematical formalism based on Markov chains using a multipriority queuing theory with cut-off discipline in order to reduce the waiting time to plug-in. We implement three planning algorithms in order to assign priority levels and then optimize the plug-in time into each EV public supply station. To the best of our knowledge, this is the first article that proposes a model that tries to save energy by planning the plug-in of EVs using a cut-off priority queuing model and a decentralized Fog architecture. We evaluate the performances of our solution via extensive simulations using a realistic energy loads from the city of Toronto, and we compare it with other recent works.

As one of the most devastating types of Distributed Denial of Service (DDoS) attacks, Domain Name System (DNS) amplification attack represents a big threat and one of the main Internet security problems to nowadays networks. Many protocols that form the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. DNS, one of the most critical elements of the Internet, is among these protocols. It is vulnerable to DDoS attacks mainly because all exchanges in this protocol use User Datagram Protocol (UDP). These attacks are difficult to defeat because attackers spoof the IP address of the victim and flood him with valid DNS responses coming from legitimate DNS servers. In this paper, we propose an efficient and scalable solution, called WisdomSDN, to effectively mitigate DNS amplification attack in the context of software defined networks (SDN). WisdomSDN covers both detection and mitigation of illegitimate DNS requests and responses. WisdomSDN consists of: (1) a novel proactive and stateful scheme (PAS) to perform one-to-one mapping between DNS requests and DNS responses; it operates proactively by sending only legitimate responses, excluding amplified illegitimate DNS responses; (2) a machine learning DDoS detection module to detect, in real-time, illegitimate DNS requests. This module consists of (a) Flow statistics collection scheme (FSC) to gather the features of flows in an efficient and scalable way using sFlow protocol; (b) Entropy calculation scheme (ECS) to measure randomness of network traffic; and (c) Bayes Network based Filtering scheme (BNF) to classify, based on entropy values, illegitimate DNS requests; and (3) DNS Mitigation scheme (DM) to effectively mitigate illegitimate DNS requests. The experimental results show that, compared to state-of-art, WisdomSDN can effectively detect/mitigate DNS amplification attack quickly with high detection rate, less false positive rate, and low overhead making it a promising solution to mitigate DNS amplification attack in a SDN environment.

The recent proliferation of Internet of Things (IoT) is paving the way for the emergence of smart cities, where billions of IoT devices are interconnected to provide novel pervasive services and automate our daily lives tasks (e.g., smart healthcare, smart home). However, as the number of insecure IoT devices continues to grow at a rapid rate, the impact of Distributed Denial-of-Service (DDoS) attacks is growing rapidly. With the advent of IoT botnets such as Mirai, the view towards IoT has changed from enabler of smart cities into a powerful amplifying tool for cyberattacks. This motivates the development of new techniques to provide flexibility and efficiency of decision making on the attack collaboration in a software defined networks (SDN) context. The new emerging technologies, such as SDN and blockchain, introduce new opportunities for low-cost, efficient and flexible DDoS attacks collaboration for the IoT based environment. In this paper, we propose Co-IoT, a blockchain-based framework for collaborative DDoS mitigation; it uses the concept of smart contracts (i.e., Ethereum's smart contracts) to facilitate the collaboration among SDN-based domains and transfer attacks information in a decentralized manner. The implementation of Co-IoT is deployed on Ethereum official test network Ropsten [1]. The experimental results confirm that Co-IoT achieves flexibility, efficiency, security and cost effectiveness making it a promising approach to mitigate large scale DDoS attacks.

In order to secure smart metering infrastructure against false data injection attacks in smart grid, we propose in this paper a new hierarchical and distributed intrusion detection system (HD-IDS). The proposed HD-IDS is based on distributed Fog architecture using three hierarchical network levels (i.e., home area network, residential area network, and Fog operation center network). At each network level, we implement an IDS; thus, the system ensures three protection and detection levels. The problem is modeled using stochastic Markov chain process illustrating the transitions between different smart meter states. The advantage of the proposed HD-IDS solution is proved using extensive simulations over different performance metrics and compared with centralized architectures. The implementation is based on real-word traces of electricity consumption of the city of Toronto.

Mobile edge computing is considered as a promising solution to augment computational capabilities of mobile devices. Mobile users can offload computation intensive tasks to edge clouds collocated with base stations. When mobile users want to offload mobile tasks through base stations, software defined network can provide a centralized control on distributed base stations and mobile devices. In this paper, we investigate the computation offloading problems in software defined network, wherein mobile users compete for limited bandwidth resources of base stations and computation resources in edge clouds. As mobile users are self-interested in making offloading decisions, we formulate the computation offloading problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamic and show that the game always admits a Nash equilibrium. Numerical results demonstrate the effectiveness of the proposed mechanism.

The blockchain is a distributed technology which allows establishing trust among unreliable users who interact and perform transactions with each other. While blockchain technology has been mainly used for crypto-currency, it has emerged as an enabling technology for establishing trust in the realm of the Internet of Things (IoT). Nevertheless, a naive usage of the blockchain for IoT leads to high delays and extensive computational power. In this paper, we propose a blockchain architecture dedicated to being used in a supply chain which comprises different distributed IoT entities. We propose a lightweight consensus for this architecture, called LC4IoT. The consensus is evaluated through extensive simulations. The results show that the proposed consensus uses low computational power, storage capability and latency.

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

Given the shortcomings of traditional networks, software-defined networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability, and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. This paper proposes HiDCoP, a distributed high-performance control plane for path computation in large-scale SDNs along with its related solutions. HiDCoP employs a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that HiDCoP outperforms existing schemes in terms of path computation time, end-to-end delay, and transmission overhead.

Initial SDN deployments endorsed a centralized architecture with a single controller that is responsible for managing entire networks. Yet, this scheme has proven to be unscalable and unreliable. Though distributed control plane architectures have gained ground recently, they bring new concerns. A principal question that should be investigated thoroughly is how to efficiently perform path computation and update flow tables in very large SDNs. In this paper, we address this problem by proposing ScalCon, a scalable hybrid SDN architecture that distributes the control plane over different controller types (i.e., Fog, Edge, Cloud). ScalCon distributes the load of path computation among all controllers and uses a multi-priority queening model and node parallelism to cut down the synchronization overhead. Simulation results show that ScalCon outperforms existing schemes in terms of path computation time, path setup latency. end-to-end delay and communication overhead.

Nowadays, blockchain is seen as one of the main technological innovations. Many applications can rely on the blockchain to secure their exchanges. However, applications with private interest cannot rely on public blockchains. First, in a public blockchain, anyone can read the whole data of the blockchain. Second, anyone can participate to the "consensus process"; the process for determining the validity of each transaction. Consortium and fully private blockchains aim to combine forcefulness of blockchains with controlled consensus process and stricter permissions for deploying a node and joining the blockchain network. In both consortium and fully private blockchains, the number of peers on the blockchain network is very small in comparison with public blockchain. Nonetheless, by targeting the nodes of blockchains, an attacker can easily manage the whole blockchain and takes control of the consensus process to validate his illegitimate transactions. In this paper, to defend blockchain nodes from DNS amplification attacks, we propose a scalable and proactive solution in the context of software defined networks (SDN), named ChainSecure. ChainSecure consists of 3 schemes: (1) StateMap, a novel stateful mapping scheme (SMS) to perform a mapping one-to-one between DNS request and response; (2) Entropy calculation scheme (ECS) to measure the disorder / randomness of data using sFlow in order to detect illegitimate flows; (3) DNS DDoS Mitigation (DDM) module to effectively mitigate illegitimate DNS requests. The experimental results show that ChainSecure protects blockchain nodes and can detect/mitigate the attack quickly to achieve high accuracy in detecting illegitimate DNS traffic making it a promising solution to protect blockchain nodes from DNS amplification attacks.

Computation offloading is envisioned as a promising solution to resource scarcity problem on mobile devices. Mobile users can offload computation intensive tasks to remote cloud with stronger capabilities. In order to execute tasks in cloud, mobile users have to upload computational data through cellular networks. When large amounts of mobile users in the same cell attempt to offload mobile tasks through the base station, the communication latencies for data transmissions may be high due to limited bandwidth resources. However, since the task completion times are constrained by hard deadlines, this restricts the feasible set of computational tasks that can be uploaded. In this paper, we propose a population game based approach to achieve efficient computation offloading for large amounts of competing mobile users, where each user is aimed to minimize his energy consumption. This game is subject to the task execution deadlines, user specific data rates, and the competition over the shared communication channel. We analyze the evolutionary dynamic of the game and show that the game always admits a Nash equilibrium. We then design a computation offloading mechanism that can achieve a Nash equilibrium of the game. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.

Given the shortcomings of traditional networks, Software-Defined Networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. In this paper, we propose DiSC, a distributed high-performance control plane for path computation in large SDNs. It endorses a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that DiSC outperforms existing schemes, including the most recent ones, in terms of path computation time, path setup latency and end-to-end delay.

In order to gather the full profits of Internet of Things (IoT) technologies, it will be necessary to provide efficient networking and computing infrastructure to support low latency and fast response for IoT applications. In this paper, we introduce a new fog architecture for IoT applications. We propose to deploy servers at the network fog level as a tree hierarchy, to efficiently use the cloud resources to serve the peak loads from devices. To schedule different devices demands, we develop an optimal workload placement method by solving a mixed nonlinear integer programming (MNIP). Then, the optimal solution is aggregated over different tiers using the Simulated Annealing Algorithm (SAA) to find out the optimal allocation using numerical iterations. The advantage of the proposed architecture is proved over different performance metrics and trough a probabilistic model and an analytic comparison.

This paper presents a decentralized scheduling architecture for Electric Vehicles (EVs) energy management based on fog computing paradigm, where optimal load balancing algorithms are implemented using priority-queuing model. The proposed architecture consists of multiple decentralized fog operation centers that assist vehicle-to-grid (V2G) communication in order to manage and schedule EVs charging/discharging requests in real-time way and to maintain the electric smart grid stability. We introduce two scheduling algorithms; 1) priority levels assignment, 2) optimal load balancing of EVs requests over fog servers. The extensive simulations and comparisons with different scenarios proved that our proposed model reduces the response time and maximizes EVs utility. In addition, the proposed scheduling algorithms optimize the energy load during peak hours, and maintain the micro grid stability using real scenarios in the city of Toronto.

Due to the highly dynamic network topology of Vehicular Ad-hoc Networks (VANETs), an effective clustering algorithm is always required to both solve the scalability problem and enhance vehicles' connections. In this paper, a discrete- time finite-state Markov chain model is proposed based on our previous clustering framework, in order to provide a comprehensive analysis of cluster stability, including cluster's lifetime and cluster member's lifetime. Moreover, the future clustering performance can be predicted through the proposed model. Numerical results are presented to evaluate the model, which show high consistency between analytical and simulation results.

In order to optimally schedule electric vehicles (EVs) energy charging and discharging demands, we propose in this paper a multi-level fog (MLF) model architecture. EVs energy demands in MLF are planned as charging and discharging calendars to handle with EVs energy demands in smart grid environment. Our work integrates a priority queuing model based on Markov chain analysis to schedule EVs energy calendars and allocate computing resources. Furthermore, we use the distributed feature of fog networks to cover micro grids. To this end, and to ensure the efficiency of resources allocation, we further propose two workload placement mechanisms for smart grid environment. Extensive simulations are performed under accurate assumptions and realistic environment based on real energy loads in the city of Toronto. The obtained results indicate the efficiency of the proposed MLF model in enhancing smart grid performance and saving EVs energy.

Spectrum sensing is a cognitive radio technique which distributes the unused spectrum hole of primary users (PU) to secondary users (SU). This technique can remarkably improve the efficiency of spectrum resources. However, spectrum waste of SUs is caused by the inconsistency of information exchanged between sender and receiver, notably the false alarm. In this paper, a false-alarm based energy control scheme is proposed to convert more energy into higher transmission rate of SUs at a high false alarm probability; otherwise, when more sensing cost is spent on SUs, the sensing process turns to be accurate enough and SUs stay quiet to avoid collision if a PU is detected occupied. An activation function is proposed to dynamically control the ON/OFF state of SUs according to the false alarm probability. Simulation results show that by using our energy activation scheme, SUs tend to contribute more, and in return obtain more gains at high false alarm risk. In this way, energy can be utilized more efficiently.

Industry 4.0 or industrial Internet of things (IIoT) has become one of the most talked-about industrial business concepts in recent years. Thus, to efficiently integrate Internet of things technology into industry, the collected and sensed data from IIoT need to be scheduled in real-time constraints, especially for big factories. To this end, we propose in this paper a hierarchical fog servers' deployment at the network service layer across different tiers. Using probabilistic analysis models, we prove the efficiency of the proposed hierarchical fog computing compared with the flat architecture. In this paper, IIoT data and requests are divided into both high priority and low priority requests; the high priority requests are urgent/emergency demands that need to be scheduled rapidly. Therefore, we use two-priority queuing model in order to schedule and analyze IIoT data. Finally, we further introduce a workload assignment algorithm to offload peak loads over higher tiers of the fog hierarchy. Using realistic industrial data from Bosch group, the benefits of the proposed architecture compared to the conventional flat design are proved using various performance metrics and through extensive simulations.

Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand-supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule user's requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto.

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

The wireless access for vehicular environment (WAVE) mandates that data packets of non-safety applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels; yet, owing to vehicles' high mobility, there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to mitigate this problem. Nevertheless, they are either inefficient or cost-ineffective. In this paper, we propose a novel approach called altruistic service channel selection (ASSCH) that compels vehicles to cooperate in order to select the least congested service channels for vehicle-to-vehicle (V2V) non-safety applications. ASSCH has three phases: 1) identifying the channel's current state (i.e., free or occupied); 2) predicting channels that are likely to be free in the near future; and 3) selecting the least used channel among them. We then propose a stochastic analytical model for the throughput of V2V non-safety applications considering various factors, including the busy channel at zero, discarded by all existing IEEE 802.11p EDCA models. Simulation results demonstrate that ASSCH outperforms existing allocation-based schemes as it incurs low capture delay, low ratio of overlapping WBSS, and high throughput. Simulation results also show that our analytical model closely matches the throughput of EDCA access categories.

Cellular network is facing a severe traffic overload problem caused by the phenomenal growth of mobile data. Offloading part of the mobile data traffic from the cellular network to alternative networks is a promising solution. In this paper, we study the mobile data offloading problem under the architecture of mobile cloud computing, where mobile data can be delivered by WiFi network and device-to-device communication. In order to minimize the overall cost for the data delivery task, it is crucial to reduce cellular network usage while satisfying delay requirements. In our proposed model, we formulate the data offloading task as a finite horizon Markov decision process. We first propose a hybrid offloading algorithm for mobile data with different delay requirements. Moreover, we establish sufficient conditions for the existence of threshold policy. Then, we propose a monotone offloading algorithm based on threshold policy in order to reduce the computational complexity. The simulation results show that the proposed offloading approach can achieve minimal communication cost compared with the other three offloading schemes.

In order to keep Electric Vehicles (EVs) far from causing peaks in power demand during the day, it is important to perform an intelligent management for EVs charging and discharging services by including different metrics in a pricing policy. In this paper, we propose a novel pricing policy for EV charging (G2V) and discharging (V2G) services, in order to reduce the peak load and the energy overflow. Our proposed policy uses cloud computing and smart grid interactions to schedule EV requests. The simulations proved that our proposed pricing policy optimizes the energy load during peak hours and satisfies EVs users and smart grid constraints.

In this paper, we present a link duration prediction method in VANETs. It utilizes AdaBoost algorithm to combine several link metrics, such as distance, difference in velocities, link lifetime, and etc., to form a predictor with a higher accuracy. The proposed method is applicable to different traffic scenarios and does not rely on any assumption of vehicles' velocity distribution. We evaluate the performance of this Adaboost-based link duration prediction algorithm by various traffic traces generated by SUMO that represents different typical scenarios. The evaluation result shows that, the proposed method effectively improve link prediction accuracy. Compared with other machine learning based solutions such as linear regression and support vector regression, the proposed method also shows less prediction error consistently.

The increasing number of intelligent electric vehicles (EVs) has stimulated challengeable problems (i.e., confidentiality, privacy) for the smart grid in the last few years. These challenges impact the exchange of sensitive information between EVs and smart grid which offer different sort of services (i.e., planning itineraries, booking charging stations (CSs)). In this paper, we propose a new architecture to secure communication exchange between EVs and the smart grid. The proposed architecture ensures both confidentiality of communications and privacy of EVs, and includes authentication and authorization in order to secure service access for EVs. Simulations were performed under different scenarios to show the performance of our proposed scheme. The results have shown the proposed architecture ensures good response time when implementing the security modules.

In recent years, the number of electric vehicles (EVs) on the road has been steadily increasing. At the same time, availability of the charging infrastructure on the road is still limited. In this paper, we propose a new scheme to guide EVs toward charging stations so as to minimize their waiting time and power energy consumption to get a charging service. The scheme uses wireless communication between EVs and the smart aggregator. It takes into account the state-of- charge (SoC) of EVs, their position, and available charging stations on the road. It also considers traffic, and occupancy of charging stations. Simulations were performed to assess the performance of our proposed scheme. Results show that the scheme effectively minimizes energy consumption and waiting times for EVs.

The large-scale deployment of electric vehicles (EVs) will provide massive energy load demand on the electric smart grid especially during peak-hours. To this end, smart grid will need to balance the charging and the discharging power among public supply stations aiming to minimize the plug-in waiting time and maintain the grid stability. To achieve these goals, we propose a charging-discharging model at public supply stations (EVPSS) based on cloud computing framework, where EVs users communicate with cloud providers to upload charging and discharging calendars. To handle EVs requests, we develop a mathematical formalism based on queuing theory considering two priority queues with two EV classes, high and low. To the best of our knowledge, this is the first paper that proposes a model that tries to solve all the aforementioned issues. Through simulation results, we demonstrate the effectiveness of the proposed approach when considering real EVs charging-discharging loads at peak-hours periods.

In this paper, we study mobile data offloading problem under the architecture of mobile cloud computing (MCC), where mobile data can be delivered by cellular, WiFi and Device-to-Device (D2D) communication networks. In order to minimize the overall cost for data delivery task, it is crucial to reduce cellular network usage while satisfying delay requirements. In the proposed model, a portion of the cellular data traffic is offloaded through WiFi and D2D networks. We formulate the data offloading problem as a finite horizon Markov Decision Process (FHMDP). We solve the problem using hybrid offloading algorithm for delay sensitive and delay tolerant applications. The simulation results show that the proposed offloading scheme can achieve minimal total cost compared with other three offloading schemes.

The Wireless Access for Vehicular Environment (WAVE) specifies that data packets of infotainment applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels. Nevertheless, owing to high mobility in vehicular ad hoc networks (VANET), there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to avoid such a scenario. Yet, they are either inefficient or cost-ineffective. In this paper, we propose ASSCH, an altruistic mechanism that impels vehicles to collaborate in order to select the least congested (i.e., used) service channel. ASSCH also includes a mechanism for WBSS termination, which is not specified in the WAVE standard. To the best of our knowledge, none of the existing works have proposed something similar. Simulation results demonstrate that our scheme handles the overlapping problem better and incurs high channel efficiency.

This paper proposes two Markovian models to analyze the performance of IEEE 802.11p EDCA mechanism for vehicle-to-vehicle (V2V) infotainment applications. The first model describes the backoff procedure and is used to compute the transmission probability of each access category (AC) while the second illustrates the contention phase after a busy channel and is used to derive the probability of collision. Both models consider backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the case where the channel is sensed busy once the counter backoff reaches 0, which is not considered in almost all of existing works in the literature. Using both models, we derive an accurate formula for the normalized throughput for each AC. Simulation results are provided to demonstrate the accuracy of our analytical model.

In the literature, there are many clustering algorithms proposed for the vehicle ad hoc networks (VANETs) to improve network stability and scalability. However, there is a lack of comprehensive comparison among them. In this paper, we show that there exists unfair comparison of clustering algorithms, in the aspect of simulators, performance metrics, simulation scenarios, and configuration of algorithms. To start the first step to tackle this problem, we propose a general framework to Fairly Compare Clustering algorithms in VANETs (FCC). Under this framework, we show that, i) misconfiguration of clustering algorithms can lead to significant performance degradation, ii) there is no winning clustering algorithms in all scenarios, iii) the design of clustering algorithms should be scenario-dependent.

Electric Vehicle (EV) and smart grids have gained much popularity in recent years. This has been enabled by the high increase of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand during the day, it is important to perform an intelligent scheduling for EVs charging and discharging by including metrics, such as price and demand-supply curve. In this paper, we propose a dynamic pricing model for EV charging (i.e., grid-to-vehicle, G2V) and discharging (i.e., vehicle-to-grid, V2G) services, in order to reduce the peak load. Our proposed model uses cloud computing architecture to schedule EV requests. We formulate our problem as a linear optimization problem and solve it using new algorithms for charging and discharging. To the best of our knowledge, this is the first paper that proposes a model that tries to solve all the aforementioned issues. The extensive simulations proved that our proposed pricing model, based on cloud computing and EVs interactions, optimizes the energy load during peak hours and satisfies EVs users and micro grid constraints.

Effective clustering algorithms are indispensable in order to solve the scalability problem in Vehicular Ad-hoc Networks (VANETs). Due to the highly dynamic network topology, an effective cluster merging scheme is always required in clustering algorithms, aiming to prevent the collapse of clusters. In the literature, there is a lack of comparison of cluster merging schemes, which makes it hard to analyze the impact of this component on clustering performance. In this paper, we analyze the existing cluster merging schemes and propose a Leadership-based Cluster Merging (LCM) scheme. Then, a comprehensive comparison of different cluster merging schemes under various traffic scenarios is presented, and our scheme is shown to achieve better performance on cluster stability.

Although the number of electric vehicles (EVs) on the road has been steadily increasing in the last few years, the problems of autonomy and limited driving range of EVs still represent a big challenge for automotive industry. In this paper, we first propose a secure architecture where EVs and the smart grid exchange information for itinerary planning and charging time-slots' reservations at charging stations. The architecture ensures privacy, and includes authentication and authorization in order to secure EVs sensitive information. Second, we introduce a new scheme for EV itinerary planning, which takes into account the state-of-charge of the EV, its destination, and available charging stations on the road. The scheme minimizes the waiting time of the EV and its overall energy consumption to attain destination. MATLAB and CPLEX simulations were performed to show the performance of our proposed scheme. Simulation proved that our model is able to optimize paths in terms of energy consumption and waiting time.

Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of electric vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for SG and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for SG based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging-discharging loads at peak-hours period.

This paper uses two Markov chains to model IEEE 802.11p EDCA throughput over service channels. A 2-D Markov chain is constructed first to describe the backoff procedure of each access category and to infer its transmission probability. Then, a 1-D discrete Markov chain is used to describe the contention phase of an access category and to derive its collision probability. Both models consider the backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the transmission opportunity (TXOP) parameter, unexploited by IEEE 802.11p, to enhance the performance of infotainment applications. Using both models, we derive an accurate model of the normalized throughput for each access category. Simulation results show that our model generates higher throughput for access categories with high priority compared to IEEE 802.11p standard.

Clustering in vehicular ad hoc networks (VANETs) is a challenging issue due to the highly dynamic vehicle mobility and frequent communication disconnections problems. Recent years' research have proven that mobility-based clustering mechanisms considering speed, moving direction, position, destination and density, were more effective in improving cluster stability. In this paper, we propose a new mobility-based and stability-based clustering algorithm (MSCA) for urban city scenario, which makes use of vehicle's moving direction, relative position and link lifetime estimation. We evaluate the performance of our proposed algorithm in terms of changing maximum lane speed and traffic flow rate. Our proposed algorithm performs well in terms of average cluster head lifetime and average number of clusters.

In this paper, we study the energy efficiency of the future 5G networks. These networks are known to be heterogeneous, containing different types of Base Stations (BSs) each one characterized by its own network coverage and delivered capacity. The aim of our work is to study and improve the energy efficiency of these networks. This is achieved by adjusting network density (i.e., number of active BSs) according to the traffic load while keeping ongoing users covered and provided with their required capacity. We first express the optimization problem and then we prove that it is NP-complete. To solve this problem, we propose a graph based heuristic to find an optimized network configuration in a polynomial time. The proposed algorithm considers both the covered area and the delivered capacity of each type of BS to guarantee users needed capacity. The strength of our method is its adaptation over multiple types of BSs and that by using the energy models of these BSs. The experiments show that the proposed method can achieve high energy efficiency by reaching 70% of energy saving during low traffic load periods within a polynomial time.

This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks (VANETs). Most existing schemes aim at reducing E2ED via greedy-based techniques (i.e., shortest path, connectivity, or number of hops), which make them prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that computes E2ED for the entire routing path before sending data messages. To do so, SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected information of delay and connectivity. Routes with the lowest aggregated weights are selected to forward data packets. Simulation results show that SCRP outperforms some of the well-known protocols in literature.

In this paper, we address the problem of energy saving in communication networks; we give more interest to heterogenous networks seeing their major role in the future wireless communication, namely, the future cellular networks (5G) [1]. The specificity of heterogenous networks, is the way that MNs (mobile nodes) switch between different communication technologies, this is known by vertical handovers. The aim of this work is to make the used protocols for vertical handover collaborative, to reach more energy efficiency for the entire network. We based our study on MIH (Media Independent Handover) protocol [2], some modifications are made into this later, in order to create a green collaborative MIH protocol. The proposed enhancements on MIH allow us to get more information about network state; these information are used as statistics to adapt network density according to traffic load. This is achieved by switching off the less efficient PoAs (Points of Attachement) within the network. Our experimentations are performed using NS2.29 [3] with Nist (National Institute of Standards and Technology) add-on which implement MIH process (802.21). The results of the proposed contribution are compared to the conventional MIH protocol and showed a significant improvement in terms of energy saving while maintaining reliability.

In recent years, many routing protocols have been proposed to enable infotainment applications for urban VANET. These schemes deploy greedy-based techniques that rely on various routing metrics (e.g., driving distance, road segment connectivity, or number of hops) to meet the requirements of the aforementioned applications, i.e., low end-to-end delay and high throughput. Yet, most of these protocols are exposed to the local maximum problem, inciting them to employ the carry-and-forward mechanism. This leads to high end-to-end delay and significant packet losses. To address this issue, we propose a Stable and Reliable CDS-based Routing Protocol (SRCP) that selects paths with high connectivity and low delivery delay. To accomplish this, SRCP builds backbones over road segments and estimates the delivery delay for transmitting data packets over them. It, then, assigns weights to the road segments and selects the routing path with the lowest total weight to forward data packets. Extensive simulations demonstrate that SRCP generates less end-to-end delay and provides better packet delivery ratio compared to existing schemes.

High Electric Vehicle (EV) penetration increases smart grid solicitation especially with various EV charging demands at peak load times. The EV charging process at public supply station (EVPSS) has to be managed in the way to promote the EV satisfaction levels while preserving smart grid stability. The waiting time for the EV charging service is an important factor in assessing the effectiveness of any interaction system between EVs and smart grid. In this paper, we present a system-guidance model to minimize the waiting time for an EV to be plugged-in for the charging service at public supply stations. We propose an algorithm for directing vehicles to charging stations in a way to minimize their searching time to join a supply station. The simulations conducted to evaluate its performance while satisfying the defined constraints proved the effectiveness of the proposed approach.

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In this paper, we study the energy consumption in heterogeneous networks (HetNets). HetNets are known to be one of the main characteristics of the future 5G networks. In this work we aim to minimize the global energy consumed within these networks by adapting the network density to the operating traffic load. We formulate this problem by expressing the energy gain (i.e., the energy saved) which should be maximized for an optimal network configuration, and then we show that finding an optimal solution for this problem is NP-Complete; thus we propose a feasible solution based on estimating the network state using queuing networks. Given that state, we proceed to adapt the network infrastructure by taking into account users required capacity. We study in our experimentations the performance of the proposed method according to different criteria; the results show significant improvements in terms of energy consumption while preserving as much as possible users' requirements in terms of capacity.

Abstract As electric vehicles (EVs) become more popular, public charging stations for such vehicles will become common. Because the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the charging waiting time. To achieve this goal, we propose two models where vehicles communicate beforehand with the grid to convey information about their charging need and location. In the first model, we develop a mathematical formalism for handling requests for charging vehicles at public charging station based on queuing theory. The second model extends the first one by considering priority queues with two EV classes, high and low, and a cut‐off service discipline. Both models are evaluated while considering mobility of vehicles in an urban scenario and time‐of‐use pricing. Finally, we propose two algorithms for directing vehicles to charging stations in a way to minimize either their waiting time to plug‐in or their waiting time to charge completion. Simulation results show the effectiveness of the proposed approaches when considering both real EV and charging station characteristics and constraints. Copyright © 2014 John Wiley & Sons, Ltd.

In the next few years, as the number of EVs will become important, the smart grid will be solicited to satisfy high power demands. To deal with this problem, efficient power charge scheduling techniquesare required. In this paper, a scheduling protocol with a load management technique is introduced. The scheduling protocol is aimed first at minimizing peak loads due to multiple EVs charging at home while using pricing policies. Second, it aims at coordinating charging and discharging processes to achieve cost optimization and improve grid stability. An analytical formulation is given for the scheduling problem with a load management strategy. The simulation results showed the effectiveness of the proposed approach in minimizing peak loads, optimization cost and improving grid stability while satisfying the defined constraints.

Wireless technologies are becoming an essential part of our daily life. These technologies are expected to provide a wide variety of real-time applications; hence, there is a vital need to provide quality-of-Service (QoS) support. One of the key mechanisms to support QoS is traffic regulation. The basic idea behind traffic regulation is to measure the network state (e.g., load) in order to adapt the rate of carefully selected application flows. In this paper, we propose a novel model, called FuzzyWMN, which can be used to implement traffic adaptation in Wireless Mesh Networks (WMNs).The objective of FuzzyWMN is to compute the rate adaptation to apply to application flows according to the current network state; it relies on two parameters to meet this objective: (1) packet delays between sources and destinations; and (2) buffer occupancy of network nodes. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to realize traffic adaptation in networks characterized by information uncertainty and imprecision due to the dynamic traffic behavior, channel interferences, etc. Extensive simulations show that FuzzyWMN achieves stable end-to-end delay and good throughput under different network conditions.

It has been reported that the IEEE 802.11 MAC protocol and the TCP congestion control are highly problematic in terms of flow starvation in wireless mesh networks (WMNs). However, the economic features of IEEE 802.11 make it the commonly-used MAC protocol in WMNs. Therefore, solving starvation at the transport layer seems to be more appropriate. Indeed, the main starvation cause in TCP is that congestion is managed as a link-based problem. However, since bandwidth is a spatially-shared resource in WMNs, congestion is a neighborhood phenomenon that should be handled using mutual cooperation within a congested neighborhood. Such cooperation considerably consumes the already scarce bandwidth of WMNs causing more congestion. In this paper, we propose a neighborhood-aware and overhead-free congestion control scheme (NICC) that solves the starvation problem without impacting the scarce bandwidth of WMNs. NICC makes use of some underexploited fields in the IEEE 802.11 frame header, without modifying the standard frame size, to provide an overhead-free multi-bit congestion feedback; being overhead-free, this feedback allows performing neighborhood cooperation without generating control overhead. Furthermore, being multi-bit, it yields source nodes a fine-grained indication of the congestion degree, providing accurate rate control. The NICC performance in terms of starvation avoidance and bandwidth efficiency is proven through extensive simulations.

In this paper, a scheduling protocol for electric vehicle (EV) home charging with time of use pricing is introduced. This work addresses the problem of EVs charging at home by adopting an appropriate charging process protocol over Power Line Communications (PLC). The scheduling protocol is aimed at minimizing peak loads on distribution feeders due to multiple EVs charging while using a time-of-use pricing policy. Energy efficiency and performance are both taken into account. An appropriate analytical formulation of the scheduling problem is given together with the proposed scheduling protocol. Simulations demonstrate the effectiveness of the proposed approach in minimizing peak loads while satisfying the defined constraints.

As electric vehicles become more popular, public charging stations for such vehicles will become common. Since the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the waiting time for users to have their vehicles charged. In this paper, we present an approach for balancing the load among charging stations in an area while minimizing the charging time of electric vehicles. We propose a model where vehicles communicate beforehand with the grid to convey information about their charging status, and develop a mathematical model of handling requests for charging vehicles at public charging station based on queuing theory. Finally, we propose an algorithm for directing vehicles to charging stations in a way to minimize their waiting time to charge completion. The simulation results show the effectiveness of the proposed approach when considering both real electric vehicle and charging station characteristics and constraints.

Wireless mesh networks (WMNs) have recently emerged as a promising technology for next-generation of wireless communications. In WMNs, admission control is deployed to control traffic loads and to prevent the wireless mesh backbone from being overloaded. Existing admission control protocols could be classified as either stateful or stateless approaches, based on network state information. Both the approaches have their limitations; the stateful models suffer from the scalability issue, while the stateless ones have the false admission problem. This paper introduces a hybrid admission control model for WMNs, based on a temporal resource reservation and three traffic regulation schemes. In particular, we propose an analytical model to compute the appropriate regulation ratio for accepted flows and to guarantee that the congestion at intermediate nodes does not exceed a threshold value. In our model, the congested node may specify the moment of session re-establishment, besides of the new rate at which the session should transmit its data packets. Using extensive simulations, we demonstrate that our model achieves high resource utilization by computing new sessions rates in a dynamic traffic-load environment, and by satisfying the quality of service (QoS) constraints in terms of delay and packets loss.

We consider the problem of distributed and secure routing for target tracking in wireless sensor networks (WSN) based on quantized sensors measurements. We propose a new method for jointly selecting the optimal communication path between slave sensors and cluster head (CH), detecting the malicious sensors and estimating the target position. Firstly, we detect the malicious sensor nodes based on the information relevance of their measurements. Secondly, we select the optimal communication route in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The computation of these criteria is based on the target position predictive distribution provided by the QVF algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.

Severe unfairness and even complete starvation may occur when using TCP-like congestion control in IEEE 802.11-based Wireless Mesh Networks (WMNs). Indeed, IEEE 802.11 is inherently unfair; however, economies of scale make it the commonly used MAC protocol in WMNs. Moreover, TCP-like protocols do not account for links interdependency within a neighborhood. In WMNs, congestion should be mutually handled using explicit coordination among neighboring contending links. Furthermore, the set of flows that should be regulated, to control congestion, must include all those traversing a congested neighborhood. However, neighborhood coordination and flows notification significantly consume the already scarce bandwidth. In this paper, we propose NICC as neighborhood-based and overhead-free congestion control protocol aiming to avoid starvation without disturbing the bandwidth resources. Instead of experiencing IEEE 802.11 as a handicap, NICC proposes a lightweight optimization of some underexploited fields in the 802.11 frames header so as to provide implicit multi-bit congestion feedback. Such feedback ensures accurate rate control without inducing additional overhead. The effectiveness of NICC in terms of starvation avoidance and bandwidth efficiency is proved through in-depth simulation.

This work presents a multi-objective algorithm for jointly selecting the appropriate group of candidate sensors and optimizing the quantization for target tracking inWireless Sensor Networks (WSN). We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering sensors selection problem. Firstly, we jointly optimize the quantization level and the group of candidate sensors selection in order to provide the required data of the target and to balance the energy dissipation in the WSN. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The quantization optimization and the sensors selection are based on multi-objective (MO) that define the main parameters that may influence the relevance of the participation in cooperation for target tracking. This optimization is also based on the transmitting power between one sensor and the CH. The best sensors selection and quantization optimization are designed to reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The simulation results show that the proposed method, outperforms the quantized variational filtering algorithm under sensing range constraint and the centralized quantized particle filter.

Wireless mesh networks (WMNs) are expected to be a next step toward future generation of wireless networks due to their rapidly deployable nature and to the wide variety of their potential use. On the other hand, the daily increase of multimedia applications over wireless networks has generated a vital need to provide Quality of Service (QoS) support in WMNs, and works in this area are not sufficient for the moment. In this paper, we propose a QoS model, named WiRS, to support real time traffic over WMNs. WiRS consists of an admission control and two traffic regulation schemes. The admission control is based on a temporary reservation process allowing multiple flows to opportunistically benefit from reserved resources when they are not used by their correspondent flow. The traffic regulation schemes aim to dynamically adjust the injected traffic into the mesh backbone in order to avoid the congestion and to maintain the QoS requirements. A service differentiation mechanism is provided also through one of the regulation schemes in order to control the best effort traffic. Extensive simulations show that our proposal is able to provide stable end-to-end delay, high throughput and improved packet delivery ratio.

The emergence of real-time applications and their widespread usage in communication have generated the need to provide quality-of-Service (QoS) support in wireless networks environments. One of the most crucial mechanisms of a model for providing QoS support is the traffic regulation. In the aim of better representing and analyzing the decision making policy of the traffic adaptation process in wireless mesh networks (WMN), we propose a novel model named FuzzyWMN. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to achieve the traffic adaptation process in the context of dynamic network events characterized by the uncertainty and imprecision information, due to the dynamic traffic behavior, channels interference, etc. The evaluation of FuzzyWMN performances, compared to AIMD-SWAN and IEEE 802.11, was studied under different network and traffic conditions. The promising results obtained from extensive simulations confirm that the traffic adaptation based on the fuzzy design can achieve stable end-to-end delay, and good throughput under different network conditions.

The problem of secure quantized target tracking in wireless sensor networks (WSN) is investigated. Due to the limited energy supplies of nodes in WSN, optimizing their design under energy constraints, reducing their communication costs, securing their data aggregation are of paramount importance. To this goal and in order to efficiently solve the problem of target tracking in WSN with quantized measurements, we propose a new method for jointly selecting the appropriate group of candidate sensors that participate in data collection, detecting the malicious sensors and estimating the target position based on quantized proximity sensors. Firstly, we select the best group in order to provide the required data of the target and to balance the energy dissipation in the WSN. This selection is also based on the transmission power between one sensor and the cluster head. Secondly, we detect the malicious sensor nodes from learned data based on the information relevance of their measurements. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.

This work presents the problem of target tracking and relay localization in wireless sensor networks (WSN) based on quantized proximity sensors. Thus, we use the quantized variational filtering (QVF) in order to estimate jointly the target position and the relay location. Recently, variational filtering has been proved to be suitable to the communication constraints of WSN. However, this problem has been proposed only for binary sensor networks neglecting the information relevance of sensor measurements and the transmission energy consumption. At each sampling instant, the adaptive scheme provides the estimates of the target position and the relay location by using the QVF algorithm. The efficiency of the proposed method is validated by simulation results in target tracking for wireless sensor networks.

Nowadays, real time traffic over wireless networks is increasing sharply. In addition, network scale is larger due to new facilities as offered by wireless mesh networks. However, the differences between (1) the infrastructure capacities, (2) the end users devices technologies, (3) the number of users, and (4) the number of real time applications are implying the need of more dynamic quality of service (QoS) models. Traditional QoS models are not always suitable to fill out the gap between the four cited points. We are mentioning more QoS degradation due to network congestion. Therefore, in this paper, we propose a novel, dynamic and persistent traffic adaption model, called FTAM. Its main role is to avoid as maximum as possible network congestion. FTAM is based on the fuzzy logic, which is known by its dynamicity and efficiency in uncertain environment. By monitoring the nodes queues evolution, FTAM estimates network congestion and makes the suitable traffic adaptation step. Extensive simulations have proved the efficiency of FTAM in terms of real time traffic QoS guarantee and preventive congestion control.

In the context of forest fire management, wireless communication is an indispensable tool. It insures events information transmission and communication between fire defenders. Traditionally, cellular networks (CNs) are used during crisis. However due to the lack of population in far forests, CNs suffer from coverage problems. In addition, the experience has proved some reachability and capacity problems of CNs. In this paper, we propose a mesh and sensor network model as a wireless communication support for forest fire management. The sensor nodes (SNs) penetrate dangerous zones to aggregate events' data and to guide rescuers to safe paths. The mesh nodes (MNs) insure communication between rescuers, and data and video transfer. Our models are empowered by a multi-agent system (MAS) to enforce the autonomy of the nodes and a fuzzy logic model to guarantee the quality of service (QoS). The simulations have proved the efficiency of our models to help in the management of forest fire.

The self-organizing and decentralized features of wireless mobile ad hoc networks make them suitable for a wide variety of applications. In this paper, we explore their use in rescue and relief applications in emergency situations. We propose to study the efficiency of some routing and MAC protocols under the client-server architecture. The presence of dynamic and adaptive routing and MAC protocols will enable ad hoc networks to be formed quickly, and ensure communications during the rescue operations. Extensive simulations were performed to show the impact of both the routing and MAC layer choice over multiple QoS parameters (delay, throughput, energy, etc.) in small and large scales rescue areas. We conclude the paper by some remarks that may be very useful for the relevant agencies to enhance the efficiency of rescue and relief operations.