The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

Internet of Vehicles (IoV), a branch of Internet of Things utilized for vehicle-to-vehicle communication, is attracting attention from both academia and industry. Vehicle nodes are thought to be constantly moving at high speeds, and as a result IoV faces major challenges including scalability, rapid topology changes, and shortest routing path. Vehicle clustering is an effective way to enhance IoV communication performance in a large network setting with complicated road conditions and huge number of vehicles. At the same, minimal clusters and selection of stable cluster heads reinforce the cluster structure stability. In this sight, we propose a new clustering algorithm based on the recent nature-inspired meta-heuristic Sparrow Search algorithm for IoV (IoVSSA). IoVSSA incorporates mobility metrics along the cluster distance to construct the fewest clusters with stable CHs. Through simulation studies, the effectiveness of proposed IoVSSA algorithm is assessed and is found to perform better than the state-of-the-art algorithms when it comes to the number of vehicle clusters and average cluster lifetime. Factors including the network grid size, average cluster lifespan, and node transmission range are taken into account for optimal clustering and the experiments make use of a variety of scenarios to compare the effectiveness of each algorithm.

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.

The Internet of Things (IoT) enables the interrelation of physical things and devices that can be accessed through the internet and it simply forms a single integrated network of various things. An IoT-facilitated smart city scenario spans several sectors, such as industrial applications, public transportation, smart grid, emergency services, health care, etc. In this paper, we propose an Intelligent Proficient Data Collection Approach (IPDCA) to deliver public data in a large-scale smart city set-up. IPDCA utilizes public vehicles as the mobile data collectors (D-collectors) that read (or collect) data from multiple Access Points (APs) and send them back to the central Base Station (BS). Moreover, IPDCA adopts a modified Bat algorithm for path finding of D-collectors, where we extend the Bat algorithm to solve our discrete optimization problem. Besides, for selecting D-collectors in smart city settings, we use a multi-objective fitness function that considers the count, travelled distance, and storage of D-collectors to ensure optimal use of resources. Efficiency of the proposed mechanism is proved through simulations.

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.