Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.

Wireless sensor networks are networks formed by a collection of tiny battery-powered sensors. Clustering has been proven to be one of the most efficient methods to lengthen the lifetime of wireless sensor networks. However, there is room for improvement in how the existing techniques select the cluster heads. In this study, we propose a Linear Programming based Cluster Head Selection (LPCHS) method that aims to optimize the energy consumption in wireless sensor networks. LPCHS selects cluster heads based on a novel linear programming model that determines which node and how many should serve as cluster heads.

Establishing an energy-balanced data routing and clustering approach is among the most fundamental steps to extend the longevity of wireless sensor networks (WSNs). This study presents an intelligent and energy-aware framework for data routing and clustering in WSN employing an adjusted Rand index (ARI)-guided discrete particle swarm optimization algorithm: ARI-DPSO. This method uses Dijkstra’s algorithm to establish energy-efficient data paths and uses the network lifetime as the ARI-DPSO’s fitness function. The discrete particle swarm optimization searches for the globally optimal cluster configuration that extends the network’s operational lifetime. The novelty of the ARI-DPSO lies in its capability to avoid premature convergence by using the ARI metric to quantify the similarity between the swarm’s global best solution and the current particles. Based on this level of similarity, ARI-DPSO employs an ARI-derived mechanism to trigger a dynamic perturbation element to force the swarm of particles to explore new search areas. The simulation results show that the ARI-DPSO improves the dynamics and diversity of the swarm, thereby maximizing the stable operational lifetime of wireless sensor networks (first node death).