The Internet of Vehicles (IoV) has evolved from the classic Vehicular Ad-hoc NETworks (VANETs) as a result of the emergence of the Internet of Things (IoT). IoV is used for communication among vehicles in real-time with their drivers, other vehicles, pedestrians, fleet management systems, and roadside infrastructure. High vehicular speeds and frequent network topology changes make vehicle communication extremely difficult on the IoV network. More constraints are imposed on IoV communication performance in a huge network environment due to the difficult road conditions and the enormous quantity of vehicles. A promising approach to improve the IoV communication performance is through vehicle clustering. Minimizing the number of clusters and identifying a reliable Cluster head (CH) are some of the most challenging tasks. In this paper, we propose a Swarm optimization-based and mobility-aware clustering method termed SOMACA. SOMACA consists of two phases clustering phase and the routing phase. During the clustering phase, we combine mobility measures and cluster distance to generate the minimum number of clusters having stable CHs and employ the Sparrow Search algorithm (SSA) for CH selection. The routing phase consists of two steps (1) Route Formation and (2) Route Upkeep. The main target for route formation step is to build a secure routing path between IoV nodes and base station (BS) by establishing an optimal list of links that are ordered from high to low, and in each round, it selects the best one. Moreover, the Upkeep step aims to update and maintain the existing connection. The performance of SOMACA is assessed using simulation experiments with various metrics including average cluster lifetime, transmission range, and network grid size. The simulation results show that SOMACA reduces the average number of clusters by 42%, 48%, 47%,9%, 22%,31%, 16%, and 43% less than CAVDO, GOA, GWOCNET, MFCA-IOV, MOGA-AWCP, HHOCNET, AMONE, and p-WOA algorithms respectively when the transmission ranges vary between 200 and 500. Moreover, SOMACA increases the average network lifetime by 11%, 12%,9%,6%, 7%, 9%, 3%, and 13% longer than the mentioned above compared algorithms, respectively.

Wireless Sensor Networks (WSNs) face significant challenges due to their constrained resources, particularly energy, necessitating efficient utilization to prolong their lifespan. This paper introduces LBAS, a novel approach that leverages the hierarchical network structure offered by star graph-based clustering to extend the working duration of heterogeneous WSNs. Our scheme considers local information parameters of nodes as criteria for selecting Cluster Heads (CHs) and forming clusters. LBAS incorporates load balancing awareness into cluster design, ensures appropriate CH selection and cluster formation, and includes a dynamic load balancing and reconfiguration phase for subsequent rounds. By utilizing the softmax function for CH election and introducing novel procedures based on graph theory concepts, the algorithm achieves efficient resource allocation and management, classifying nodes into three load-based classes. Transformation of the load balancing process into linear equations further enables adaptive network operation. Through extensive simulations, we demonstrate the effectiveness of our LBAS scheme in prolonging the network’s lifetime compared to other clustering algorithms. These findings emphasize the potential of our approach in extending the operational duration of WSNs while ensuring efficient resource utilization.

In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.

The versatility and diverse applications of IoT-based Heterogeneous WSN (HWSN) technologies make them valuable tools for achieving sustainability goals in Sustainable Smart Cities (SSCs). Proper management of underlying heterogeneous architecture is crucial for the successful operation of smart applications. To prolong the WSN lifespan and avoid failures, effective energy management is essential. Although researchers are continually exploring heterogeneity in WSN, it gets more and more important to create cost-effective paradigms that cover multiple facets of SSC while ensuring their stability and reliability. The concept of Dominating Sets (DS) in a graph can be leveraged to minimize resource utilization in WSNs by arranging nodes into disjoint DS, with only one set executing duties at any given time. In this work, we propose a novel technique for IoT-based HWSNs enabled SSC, called EADDSA, utilizing the DS concept to plan the sleep-and-awake scheme for heterogeneous nodes, based on their resource capabilities. We propose a new algorithm, called Energy Attentive Algorithm (EAA), to find disjoint DSs that are energy-aware. EAA algorithm attentively tries to form the set that maximizes lifespan while adhering to DS conditions in each iteration. EADDSA further incorporates an effective DS scheduling strategy to enhance the HWSN lifetime by establishing operational guidelines for each round, taking into account the estimated lifetimes and the designated number of working rounds for each DS. This enables efficient allocation of data sensing and gathering tasks across the network minimizes resource usage, and extends network lifetime.

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.