The Industrial Internet of Things (IIoT) has transformed industrial processes by allowing real-time data monitoring and automation. While IIoT offers many operational advantages, it is still at risk of cyberattacks like Distributed Denial of Service (DDoS) attacks. This paper introduces a novel Hybrid Explainable AI for DDoS Attack Detection (HEAD) system. The HEAD system combines deep learning, feature attribution, and model optimization within IIoT networks. Unlike earlier systems that rely on tree-based or black-box models without post-training optimization, the HEAD system introduces three main innovations. Firstly, it applies a combined explanation-based feature selection approach using SHAP and LIME to identify both global and local feature importance. The geometric mean of the normalized SHAP and LIME values is used to rank and select the most informative features. Secondly, the HEAD system uses a feedforward deep neural network trained on the selected features to learn important traffic patterns. It maintains high detection accuracy while keeping the model simple and efficient. This replaces traditional ML models with a more compact and flexible architecture. Thirdly, the system applies SHAP GradientExplainer to the trained model to identify neurons in the first hidden layer that are influenced by the input features. These neurons are pruned to create a lightweight version of the model that reduces computation without compromising accuracy. Evaluation on the HL-IoT, ToNIoT, and Edge-IIoTSet datasets shows that the HEAD system attains over 94% accuracy and enhances model efficiency through SHAP-guided pruning. To support real-world deployment the HEAD system provides an interactive interface that shows SHAP visualizations and training results. This helps network administrators monitor predictions and improve detection policies. This design ensures that HEAD is not only accurate but also transparent and suitable for deployment in real IIoT networks.

As Internet of Things (IoT) networks continue to grow in complexity and scale, ensuring reliable service delivery while defending against cyber attacks such as Distributed Denial of Service (DDoS) has become increasingly critical. IoT networks, with their resource-constrained devices, diverse traffic patterns, and real-time requirements, amplify the limitations of existing DDoS detection and mitigation solutions. These solutions often prioritize classification accuracy, but rely on static policies that do not adapt to evolving traffic behaviour or prioritize critical services. To address these challenges, Intent-Based Networking (IBN) offers a promising approach by enabling networks to dynamically align with high-level service goals, such as prioritizing control traffic or ensuring low-latency communication. However, current security solutions lack integration with IBN, resulting in a gap in context-driven, intent-aware DDoS mitigation. To address this, the paper proposes an intelligent intent-aware system for DDoS attack detection and mitigation (INACT) in IoT networks. The INACT system introduces a dual-output deep learning model that classifies both the type of traffic (benign or malicious) and its operational intent (e.g., control, security, or bandwidth priority), using a multitask learning approach. The INACT system uses a gradient-based method to select the most relevant features, allowing it to run smoothly on lightweight edge devices. To take immediate and meaningful action, the system includes a controller that applies different mitigation strategies depending on the intent of traffic. This ensures that critical services are protected first and that nonessential traffic is managed with minimal disruption during the attack response. The INACT system is evaluated using benchmark datasets such as HL-IoT and CICIoT-2023 and is deployed on a real testbed. The INACT system achieves high detection and intent classification accuracy while maintaining low latency, resource usage, and mitigation effectiveness.

Blockchain technology, particularly Hyperledger Fabric (HLF), has emerged as a promising solution to enhance security and privacy in various domains, including Internet of Things (IoT) networks. Conflicting transactions in a HLF-based IoT network occur when multiple transactions attempt to modify the same asset or data concurrently. Conflicting transactions can lead to data inconsistencies, because the network may be unable to determine the correct order or the most preferred valid transaction. Existing conflict resolution mechanisms in HLF-based IoT networks often introduce considerable transaction latency, detect and resolve conflicting transactions in the late stages of the transaction lifecycle (ordering and validation), or require significant changes to the underlying HLF blockchain platform. To overcome these limitations, we propose an Early Conflict Resolution (ECR) mechanism that detects and resolves conflicts during the endorsement stage. The ECR mechanism uses a local cache (Sync.Map) and a dependency graph to efficiently detect conflicts by analyzing the Read-Sets (RS) and Write-Sets (WS) of transactions. ECR resolves conflicts in the detected conflicting transactions through transaction reordering or sequential processing. It also executes non-conflicting transactions in parallel to speed their processing. Our results show that the ECR mechanism improves transaction latency and the success rate for varying conflict rates, block sizes, and IoT devices compared to existing mechanisms.

The deployment of Wireless Sensor Networks (WSN) in critical infrastructure, such as Small Modular Reactors (SMRs), faces cybersecurity threats like Distributed Denial of Service (DDoS) attacks that can overload these networks and disrupt monitoring and control functions. Current DDoS detection systems often suffer from high false positive rates, neglect domain-specific operational constraints, and rely on centralized architectures that pose privacy risks, making them less suitable for distributed Internet of Things (IoT) environments. To address these issues, we propose a novel Domain-informed Hierarchical Federated Learning (DHFL) framework for WSN used in SMR monitoring and control applications. Our framework features a dual-branch bidirectional Long Short-Term Memory (LSTM) architecture comprising of two parallel processing branches with network-specific constraints, facilitating precise detection of DDoS attacks. It includes differentiable penalty functions to enforce domain-aligned behaviour and employs adaptive trust scoring to evaluate the reliability of individual nodes. These elements operate within a hierarchical Federated Learning (FL) structure organized into three tiers: sensor nodes, local aggregators, and a global coordinator, allowing collaborative training that preserves privacy. Unlike earlier approaches, our method not only maintains privacy by ensuring that raw sensor data never leaves the local nodes and only model updates are shared but also considers the operational importance and trustworthiness of each node through tier-weighted aggregation. Tested on the CICIoT2023 dataset, our system achieved 93.4% accuracy, 94.5% precision, 97.5% recall, 95.5% F1-score, and 98.9% AUC, surpassing state-of-the-art FL methods in both performance and efficiency. Furthermore, it converged in fewer communication rounds (30–50) with reduced communication costs (from 45 MB to 30 MB per round). Our framework can differentiate between normal reactor transients and actual attacks, making it suitable for mission-critical SMR cybersecurity.

Conflicting transactions pose significant challenges in Hyperledger Fabric (HLF)-based IoT networks, affecting performance and introducing security vulnerabilities that can facilitate malicious attacks. Traditional conflict resolution mechanisms resolve conflicts in the later stages of transaction processing (i.e., the ordering or validation stages), resulting in increased transaction latency, which impacts delay-critical IoT applications. This paper proposes an Early Conflict Resolution (ECR) mechanism that integrates conflict detection and resolution at the endorsement stage, enhancing throughput and reducing transaction latency. This paper also explores the impact of conflicting transactions on blockchain attack vectors, focusing on four pivotal attacks-block withholding, double spending, balance attacks, and Distributed Denial-of-Service (DDoS)-simulated to analyze their exploitation of transaction conflicts and their impact on IoT networks. The results show that the ECR mechanism significantly improves the success rate and transaction latency compared to existing mechanisms.

The rapid expansion of the Internet of Things (IoT) has introduced significant advancements in real-time monitoring and management, but it has also brought new security challenges, particularly from Distributed Denial of Service (DDoS) attacks. These attacks pose a persistent threat to IoT networks, especially impacting resource-constrained edge nodes. This paper presents a novel Genetic Algorithm and Game-based Defense (G2D) model, designed to identify and adaptively apply optimal strategies to defend against DDoS attacks. The G2D model integrates genetic algorithms and game theory to dynamically determine equilibrium strategies, where defense mechanisms such as high- and lowinteraction honeypots and rate limiting are adjusted based on the intensity of incoming attacks to optimize resource allocation. By modeling attacker-defender interactions with bounded rationality, the system continuously refines its strategies over multiple iterations, adapting to evolving attack patterns. Simulation results indicate that the G2D model offers stable and adaptive defenses, achieving higher average payoffs, and a reduced outcome variance. Additionally, the model shows robust adaptability across different attack volumes, making it a reliable solution for enhancing IoT network security.

Integrating virtual wireless sensor networks (VWSNs) with the Internet of Things (IoT) improves the quality of information (QoI) and quality of service (QoS). It manages wireless interference, critical to providing efficient and reliable services. Among the challenges in IoT-WSN virtualization, the survivable virtual network embedding (SVNE) problem stands out, as it efficiently maps a virtual network request (VNR) onto a WSN substrate while considering potential substrate failures and network security standards. This paper proposes a trust-aware fault recovery mechanism to address the security and survivability of virtualized IoT-WSN applications against physical infrastructure failures with two heuristic and intelligent approaches. Our proposed heuristic approach utilizes a node importance measurement strategy for faulty nodes based on the technique for order of preference by similarity to the ideal solution (TOPSIS) method. On the other hand, in our intelligent approach, we apply the deep Q-Learning (DQL) method to ensure end-to-end failure recovery for both nodes and links and improve physical resource utilization. To maintain cost efficiency, when a VNR experiences failure due to a fault in the physical infrastructure, its operation is restored through node/link migration without considering any backup resources. Our simulation results demonstrate that the proposed strategy effectively ensures the survivability of the VNRs, mitigates failures with our proposed failure recovery algorithms, and enhances the VNR acceptance rate.

The rapid integration of the Internet of Things (IoT) into various systems, driven by advanced sensor networks, has dramatically improved real-time data monitoring and overall management across multiple industries. However, this integration also exposes IoT networks to various security vulnerabilities, mainly Distributed Denial of Service (DDoS) attacks, which can severely disrupt many services. Therefore, it is necessary to develop robust defence strategies for IoT networks. Traditional security measures often need to consider the strategic aspects of cybersecurity, where quick and precise decision-making is crucial. Given the adversarial nature of the interactions between attackers and defenders, selecting the most effective defence strategy to maximize benefits remains a challenge. To address this issue, this paper introduces the DDoS Defence Strategy Model (DDSM), which strategically uses game theory to select optimal defence mechanisms in IoT networks. The model dynamically adapts defence strategies based on the intensity and characteristics of the attack, optimizing the deployment of high-interaction and low-interaction honeypots and rate-limiting mechanisms. The DDSM model uses a gradient-based approach to achieve Nash equilibrium, adapting to evolving attack patterns to ensure efficient resource utilization and reduce operational overhead. The simulation results confirm the effectiveness of the model in selecting optimal defence strategies and maximizing defensive payoffs. The DDSM game model is designed to find the best combination of defences for IoT networks against high- and low-volume DDoS attacks, ensuring the continued availability of critical services.

Integrating virtual wireless sensor networks (VWSNs) with the Internet of Things (IoT) is crucial in advancing e-health applications. It significantly improves the quality of information (QoI) and quality of service (QoS), and manages wireless interference, which is critical to providing efficient and reliable healthcare services. Among the challenges in WSN virtualization, the survivable virtual network embedding (SVNE) problem stands out, as it efficiently maps a virtual network request (VNR) onto a WSN while considering potential substrate failures and network security standards. This paper proposes a trust-aware fault recovery mechanism to address the security and survivability of physical sensor node failures of IoT-based e-health applications. Our proposed mechanism utilizes a node importance measurement strategy for faulty nodes based on the technique for order of preference by similarity to the ideal solution (TOPSIS) method. To maintain cost efficiency, when a VNR experiences failure due to a physical node’s fault, its operation is restored through node migration without considering any backup nodes. Our simulation results demonstrate that the proposed strategy effectively ensures the survivability of the VNRs, mitigates node failures with our proposed failure recovery algorithm, and enhances the VNR acceptance rate.

Integrating the Internet of Things (IoT) into the healthcare sector through the Internet of Medical Things (IoMT) has significantly enhanced patient care and the functionality of medical devices. However, this integration has introduced new challenges in cybersecurity, especially in detecting Distributed Denial of Service (DDoS) attacks. While various Machine Learning (ML)-based methods have been proposed to detect DDoS attacks, they face difficulty detecting both high-and low-volume DDoS attacks simultaneously. Additionally, there is a need to identify the optimal defense strategy to safeguard IoMT networks. This paper introduces a Lightweight And Optimal Defense System (LAMDA) for IoMT networks using a novel and efficient feature selection method called Threshold Feature Selection (TFS) with tree-based ML models. The system incorporates a game theory approach to identify the most effective defense strategies, enabling rapid and accurate decision-making during cyberattacks. The performance of the LAMDA system is evaluated using various datasets containing both high-and low-volume DDoS attacks. Results indicate that the LAMDA system, mainly when using the Random Forest model, achieves an accuracy rate of over 93% in detecting such attacks.

Peer-to-Peer (P2P) energy trading, facilitated by prosumers who both produce and consume energy, provides a new type of for energy trading. Prosumers generate renewable energy in various environments, from industrial to residential. Traditional centralized energy trading methods pose risks, such as single point of failure and security issues. In contrast, de-centralized energy trading methods that use blockchains provide high security and reliability. However, the blockchain technology is not without limitations; in particular, the blockchain-based authentication mechanisms face three limitations, namely, they do not fully protect prosumer privacy due to unencrypted transactions, they are susceptible to multiple security attacks, and their authentication processes demand high computational and communication resources. To address these limitations, this paper proposes a novel Privacy-Preserving Mutual Authentication (PPMA) mechanism for P2P energy trading in smart grid networks. By employing Elliptic Curve Cryptography (ECC), symmetric encryption, and hash functions, the PPMA mechanism provides secure, privacy-preserving, and cost-effective mutual authentication for prosumers in P2P energy trading. When integrated with a permissioned blockchain and smart contract, PPMA aims to facilitate secure and scalable P2P energy trading. The efficacy of the PPMA mechanism is evaluated through comprehensive security and cost analyses.

In tactical Internet of Things (IoT) applications, the broadcast nature of wireless sensor networks (WSNs) makes it easy to eavesdrop on their traffic. Furthermore, adversaries can access the sensor nodes to intercept and eavesdrop on critical wireless transmission. Research in this area focuses on reducing the eavesdropping probability through specific methods, such as encryption and transmission power control. However, eavesdropper detection techniques in WSNs do not exist in the literature. This article proposes a novel enhanced active eavesdroppers detection (EAED) system for homogeneous multihop WSNs. The EAED system consists of a monitoring module and a detection engine module. The Monitoring module plays a vital role in the EAED system to provide accurate measurements for the detection engine module. We propose three monitoring architectures for this measurement: 1) static monitoring nodes; 2) unmanned aerial vehicles (UAVs)-based monitoring; and 3) neighborhood monitoring. To find the optimal locations for static monitoring nodes, we use a genetic algorithm (GA). We also use the Hamiltonian path planning to calculate the flight path for UAVs. The detection engine module utilizes a lightweight anomaly detection method that employs the$Z$-test method and runs on edge devices. We analyze and discuss the network overhead, advantages, and disadvantages of different monitoring architectures. According to the simulation results, the EAED system can detect active eavesdroppers with a high acrlong DR$({\ge }90\%)$and a low false-positive rate$({\leq }5\%$) and outstanding performance (${\mathrm{ AUC}}\approx 0.97$).

Providing low latency, high security, and high resource utilization for Internet of Things (IoT) networks is challenging due to the heterogeneous nature of these networks and the need for more standardization in security algorithms. Current edge computing-based IoT solutions decrease network latency and improve resource utilization but do not provide adequate security because they offer multiple attack surfaces for adversaries. Recent work uses blockchain technology to provide better security in IoT networks. However, blockchain-based solutions suffer from scalability problems and can increase latency. Sidechains are parallel blockchain networks typically used to increase the scalability of blockchain networks. We propose a novel Sidechain-based Access control and Trust evaluation mechanism for IoT networks (SATI) to decrease network latency and improve scalability, security, and energy efficiency. SATI uses a sidechain with the blockchain network to improve its scalability. It also uses edge computing to provide low network latency and high resource utilization in terms of CPU and memory usage. In addition, trust evaluation and attribute-based access control mechanisms are used to improve the security of the IoT network. We compare our work with existing mechanisms in terms of scalability, security, latency, and CPU and memory usage. In addition, we perform a formal security analysis of the SATI mechanism using reduction-based analysis and the Scyther verification tool.

Network virtualization in wireless sensor networks (WSNs) enables the utilization of shared sensing capabilities in many industrial Internet of Things (IIoT) applications. Efficient assignment of WSN resources can be achieved through virtual network embedding (VNE) while considering the quality of information (QoI) (as the accuracy of sensing), the quality of service (QoS) (as the reliability), and wireless interference handling constraints. The more the virtual networks can be mapped onto the substrate network, the more revenue the infrastructure provider will acquire. Therefore improving the acceptance rate of VNE is essential. However, this may lead to occupying more network resources and links and increase the cost, especially in dense networks. On the other hand, the shared and complex nature of VNE exposes WSNs to security risks. In this paper, we develop a novel offline distributed trust-aware virtual wireless sensor networks (DTA-VWSN) algorithm to maximize the virtual networks acceptance rate while minimizing the cost. Our proposed algorithm considers the QoI, QoS, and security, by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Since centralized algorithms suffer from scalability issues, this paper presents our new approach to the virtual network embedding problem in a distributed manner. In this paper, we use the techniques of multiagent systems as a well-known approach for distributed systems to scale these algorithms to network size. Our DTA-VWSN algorithm achieves a high-quality sub-optimal solution in a short duration, enabling us to investigate the tradeoff between solution quality and search time. Our algorithm is also evaluated in large-scale network scenarios to verify all enforced limitations by the WSN substrate. Simulation results show that DTA-VWSN improves the virtual network acceptance ratio, cost, and execution time in large-scale substrate networks. For instance, in a scenario with 150 substrate nodes and 6 VNRs, the accuracy of DTA-VWSN compared with the optimal value in terms of the VNR acceptance rate and the cost is 91.6% and 94.5%, respectively, while the execution time is 68.35% faster.

Network virtualization in wireless sensor networks (WSNs) enables the utilization of shared sensing capabilities in many industrial internet of things (IIoT) applications. Efficient assignment of WSN resources can be achieved through virtual network embedding (VNE) while considering the Quality of Information (QoI) (as the accuracy of sensing), the Quality of Service (QoS) (as the reliability), and wireless interference handling constraints. The shared and complex nature of VNE exposes WSNs to security risks. In this paper, we develop a novel offline distributed trust-aware virtual wireless sensor networks (DTA-VWSN) algorithm that considers the QoI, QoS, and security, by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Since centralized algorithms suffer from scalability issues, this paper presents our new approach to the virtual network embedding problem in a distributed manner. We use the techniques of multi-agent systems as a well-known approach for distributed systems to scale these algorithms to network size. Our simulation results show that DTA-VWSN improves the execution time of embedding algorithms, acceptance ratio, and cost in substrate networks.

High and low-volume Distributed Denial of Service (DDoS) attacks are critical threats to many Internet of Things (IoT) networks. Low-volume attacks gradually overwhelm the device’s resources, whereas high-volume attacks suddenly flood the device’s resources, causing a decline in Quality of Service (QoS). Researchers have proposed various methods to detect DDoS attacks based on statistical and Machine Learning (ML) approaches. Research has also shown that statistical approaches are more efficient for IoT networks as they are simpler to develop and have better real-time performance. However, most existing ML and statistical-based detection methods are effective for either high-volume or low-volume attacks but not for both. This paper proposes a novel Entropy and Divergence-based DDoS Attack Detection (EDDAD) system that uses a statistical approach to simultaneously detect high and low-volume DDoS attacks with high accuracy. The EDDAD system computes entropy and Kullback-Leibler (KL) divergence of flow features in a time window to detect malicious traffic in IoT networks with adaptive thresholds that utilize statistical information. Our analysis of experimental results from a real testbed demonstrated that the EDDAD system is effective and can achieve detection accuracy of greater than 90% for both high and low-volume DDoS attacks.

Eavesdropping attacks can threaten the privacy, confidentiality, and authenticity of Wireless Sensor Networks (WSNs). Since the broadcast nature of the wireless channel is vulnerable to overhearing by adversaries, detection of the presence of eavesdroppers in wireless networks can mitigate the impacts of more harmful attacks. Traditionally, researchers have tried to decrease the risk of covert eavesdropping by cryptographic protocols, information-theoretic solutions, or controlling transmission range. These approaches are not suitable for the resource-limited WSNs. In this paper, we propose a novel Active Eavesdroppers Detection (AED) system for multi-hop WSNs. Our proposed system utilizes an out-of-band Unmanned Aerial Vehicle (UAV)-assisted monitoring system in WSNs to measure intranode delays. In addition, the detection system is equipped with a lightweight detection engine, which runs at edge devices, using the Z-test algorithm. We show the effectiveness of our proposed system through simulations. The results show a high detection rate and a low false-positive rate.

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

Markov-based analytical modeling has been used extensively to model the operation of the MAC protocol of the IEEE 802.15.4 standard under diverse assumptions. These models reveal the parameters that control the behavior of each node in the network such as end-to-end latency, reliability and power consumption. Focusing on improving a certain metric is highly dependent on the type of application the Wireless Sensor Network (WSN) is designed to support. Although reducing power consumption is a primary design factor in WSNs, the emergence of delay and reliability critical applications such as Smart Grid, healthcare, and the Intelligent Transportation System (ITS) calls for more stringent latency and reliability considerations. In this paper, we develop an optimization model for clustered WSNs to minimize the end-to-end delay and power consumption while maintaining certain levels of reliability. We formulate our optimization problem based on mathematical expressions derived from a Markov-based model. Our results indicate that the lower bounds to expect on latency and power consumption, under constraints on reliability and WSN cluster size, are within the limits required by latency-critical applications.

Wireless sensor networks (WSNs) are effective tools in many mission-critical applications, such as health care, defence applications, Intelligent Transportation System (ITS), smart grid and industrial condition monitoring. Low power consumption is the main attractive feature of WSNs, hence, protocols and algorithms implemented in WSNs should always maintain low power operation. Cybersecurity of WSNs in mission- critical applications is one of the major design aspects of these networks. However, implementing security mechanisms in WSNs is a challenging task due to the limited computation and power resources of the sensor nodes. Therefore, WSN security mechanisms should not only focus on maintaining high reliability and throughput needed by mission- critical applications, but also should maintain low power operation. In this paper, we develop a low power WSN cybersecurity mechanism suitable for mission-critical applications. Our mechanism can detect and isolate various attacks, such as denial of sleep, forge and replay attacks in an energy efficient way. Simulation results show that our mechanism can outperform existing techniques in terms of power consumption and reliability.

The Enhanced Distributed Channel Access (EDCA) and the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms are used in the IEEE 802.11p standard to support Quality of Service (QoS) and reliable communication in Vehicular Ad hoc Networks (VANETs). An essential part of the CSMA/CA process is when a packet in an Access Category (AC) waits for an Arbitrary Inter- Frame Space (AIFS) period of time before engaging in the the backoff phase. According to the standard, AIFS values are fixed and deterministic, they do not guarantee strict priority for ACs especially when these values are combined with the Contention Window (CW) size of the backoff phase. On the other hand, the AIFS values are not adjustable, they do not adapt to the current status of the medium. In this paper, we propose two algorithms for AIFS value assignment, the Strict Priority Algorithm (SPA) and the Adaptive AIFS Algorithm (A3). With SPA, the AIFS values are fixed, but they are determined according to a mathematical formula that ensures strict priority level among the ACs. With A3, the AIFS values are adaptively changing depending on the value of the collision probability. AIFS still maintains a strict priority level assignment among the ACs.

Vehicular-to-Vehicular (V2V) and Vehicular-to-Infrastructure (V2I) communications in Vehicular Ad hoc Network (VANET) environment rely mainly on Medium Access Control (MAC) to minimize the delay and maintain reliable access to the communication medium. IEEE 802.11p standard is developed to achieve these goals. However its performance deteriorates drastically in large networks due to the high level of contention between the vehicles. In this paper, we propose a Quality of Service (QoS) based MAC scheme for high intensity vehicular networks to maintain low delays, high throughput and support QoS provisioning for critical traffic. We develop a model to partition the network into clusters managed by Cluster Heads (CHs), the CHs form a multi-layer architecture where the Road Side Unit (RSU) is located at the root of the tree. We propose a QoS Distributed Time Synchronization (QDTS) mechanism to manage the Time Slot (TS) allocations in a distributed manner. We develop three communication scenarios to evaluate our mechanism in terms of average end to end delay and average throughput. Our results show that our mechanism outperforms the IEEE 802.11p standard, the average end-to-end delay is reduced and the average network throughput is increased.

The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) is an integrated algorithm employed in the Enhanced Distributed Channel Access (EDCA) in the IEEE 802.11p protocol. CSMA/CA manages the access of packets to the medium and its main role is to avoid collisions among them. An essential process of CSMA/CA is the backoff period in which the packets has to wait for a random number of Time Slots (TS) before attempting to access the medium. The backoff operation is based on a simple deterministic approach that does not consider the status of the medium at the time of transmission. In this paper, we propose an Adaptive Backoff Algorithm (ABA). ABA is based on a probabilistic approach that takes into account the current situation of the medium. ABA is calculated as a function of the packets probability of collision. We present two algorithms ABA1 and ABA2, the function of the former is directly proportional to the collision probability however the function of the latter is exponentially dependent on the collision probability. We develop a Markov-based analytical model to evaluate ABA1 and ABA2, and study the performance of each system.

The ascending increase in the numbers of Plug-in Electric Vehicles (PEVs) in the world brought forward many new challenges to the power grid and utility networks. Some of these challenges are related to finding and managing additional power resources for these PEVs. Green power is one of the alternatives but, we still need to find a cheap way to store this power, PEVs could play a significant role in storing power at a certain time and use it at another time. Never the less, it can play the same role with electric power from grids so it can store power from off peak time to peak time. This role might help the grid to fulfill the growing demands. In this paper, we propose flexible charging and discharging algorithm that effectively addresses and solves the problem of power demand on peak time using the PEV's batteries as a source for backup energy storage. The results show significant enhancement in the power consumption without affecting the performance of electric vehicle.

Plug-in Electric Vehicles (PEVs) are expected to be widely utilized in the near future if issues related to the availability of charging infrastructure are resolved and if PEVs are efficiently integrated with the smart grid. The Vehicle-to-Grid (V2G) system is an emerging technology that enables the communication and control between PEVs and the smart grid. This promising concept is designed to provide the vehicles with information about where and when to charge their batteries, and allows the smart grid to acquire power from a PEV. An essential element to the success of V2G systems is reliable and secure communication system. Wireless communications in highly mobile V2G environment introduce serious challenges, such as reliability and real-time communication. In this paper, we present a comprehensive analysis of the impact of speed on the end-to-end delay and throughput in V2G communication scenarios. We focus on situations where authentication is performed when essential information such as payment data is exchanged between PEVs and its charging infrastructure. Furthermore, we present realistic delay analysis of the proposed communication infrastructure. Our simulation results show the impact of traffic density and speed on both the end-to-end delay and the throughput. We draw recommendations based on our test scenarios and simulation results.

Wireless Sensor Networks (WSNs) have been proposed for various monitoring applications including environmental, industrial, military and health care. The use of WSNs with cluster-tree topologies for such applications solves the limited coverage issue of the wireless sensor devices and allows them to be deployed in wider area. WSNs with cluster-tree topologies suffer from various problems including accurate synchronization of beacons used in the beacon enabled mode in the IEEE 802.15.4 standard and providing Quality of Service (QoS) to delay-aware applications. In this paper, we present a Time Slot Control (TSC) scheme that can adaptively manage the allocation of time slots in the beacon enabled mode of operation to provide QoS grantees to delay critical traffic. Our proposed scheme can improve the end-to-end delay and throughput of selected traffic types by managing the time slots between sensor devices in an optimum way.

Electric vehicles (EVs) are expected to greatly reduce the carbon emissions from surface transport if they are widely used and efficiently charged. One of the main limitations of EVs is their limited range and relatively long recharging times. This limitation is closely associated with the current battery technologies used in the EVs. In order efficiently utilize the EVs, their charging schedules and locations must be effectively integrated within the smart grid. Real-time and reliable integration of EVs with the smart grid could solve problems related to demand response, cost and time of charging. In this paper, we propose a Quality of Service (QoS) scheme for Charging EVs (QCEV) in a smart grid environment. The proposed scheme provides centralized QoS differentiation to EVs that are communicating with an Access Point (AP) in situations where immediate EV battery charging is required. Our simulation results show that QCEV could significantly improve the performance of the wireless communication network especially in dense deployments.

Wireless Sensor Networks (WSNs) are anticipated to become the preferred tools of choice for monitoring and controlling power utility assets in the smart grid due to their versatility. However, in some smart grid monitoring applications, data generation rates could fluctuate rapidly due to the sudden occurrence of critical faults or failures in the monitored equipment. As a consequence, critical data could experience excessive delays because of this increase in the packet arrival rates. In this paper, we present an Adaptive Guaranteed Time Slot (GTS) allocation scheme (AGTS) for IEEE 802.15.4-based WSNs used in high traffic intensity smart grid monitoring applications. AGTS scheme can adaptively reduce the end-to-end delay and flexibly tune the GTS to provide the required Quality of Service (QoS) differentiation to delay critical smart grid monitoring applications. The proposed scheme can adaptively allocate the needed GTS to nodes transmitting high priority traffic or draw back the unneeded GTS.

The use of Wireless Sensor Networks (WSNs) to monitor and control power utility assets in the smart grid is gaining increasing popularity due to their various desirable features. WSNs with multihop cluster tree topologies solve the limited coverage problem of sensor nodes. However, in smart grid monitoring applications, data rates could increase suddenly due to the occurrence of critical faults in the monitored environment. Critical data transmission could experience excessive delays because of this increase in the packet arrival rates. Therefore, there should be an optimum operating point in the network where the network could handle high packet arrival rates and maintain low latency at the same time. In this paper, we present an optimization scheme that can achieve low latency while maintaining high reliability values. Furthermore, we design our scheme to provide Quality of Service (QoS) differentiation to high priority and delay critical data. Results show that our proposed scheme significantly reduces the delay while providing high reliability and incurring low energy consumption.

Certain Wireless Sensor Network (WSN) applications such as patient monitoring, smart grid, and equipment condition monitoring require accurate estimation of specific WSN parameters such as end-to-end delay, reliability and power consumption. The estimation of these parameters calls for an accurate and lightweight WSN model that is suitable for the low processing capabilities of sensor nodes. In this paper, we present a Markov-based model for WSNs that considers the impact of inserting a MAC-level finite buffer on the performance of WSNs. We perform a comprehensive performance analysis of the end-to-end delay, reliability and power consumption using different traffic and network conditions in star and cluster-tree WSN topologies. Furthermore, we test the accuracy of our model by conducting extensive simulations in environments that are consistent with the analytical model.

In critical infrastructure monitoring applications, the packet arrival rates of a Wireless Sensor Network (WSN) may abruptly increase when cascaded failures are observed in the monitored environment. WSNs with cluster-tree topologies could experience excessive delays because of this increase in packet arrival rates. Therefore, there should be an optimum operating point in the network where the network could accommodate high packet arrival rates with low latency. In this paper, we propose an adaptive scheme that can achieve low latency while maintaining high reliability values in cluster-tree based WSNs. Furthermore, our scheme provides Quality of Service (QoS) differentiation to high priority data. Analytical and simulation results show that our scheme significantly reduces the delay while maintaining high reliability and energy efficiency values.

The Quality of Service (QoS) in smart grid communications especially in monitoring smart grid assets is becoming significantly important for emerging smart grid applications. Wireless Sensor Networks (WSNs) are expected to be widely utilized in a broad range of smart grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health. WSNs protocols are not designed to provide QoS provisioning for monitoring applications. Thus, the use of WSNs in transmitting delay-critical data from smart grid assets calls for data prioritization and delay-mitigation schemes. In this paper, we propose a delay-responsive, cross layer scheme with linear backoff (LDRX) mechanism to address delay and service requirements of the smart grid monitoring applications. The LDRX scheme is designed to operate in cluster-tree WSN topology that is suitable for monitoring wide areas such as electrical substations or large installations. We show that LDRX has greater impact on delay reduction compared to previously proposed WSNs delay reduction schemes.