Optimization Network Lifetime Through Residual Energy-Based Cluster Head Selection in IoT-Enabled WSNs

Abstract

Wireless Sensor Networks (WSNs) have gained an emerging importance in different application domains especially in event tracking and monitoring. The sensor nodes in WSNs are observed to have shorter lifetime due to the continuous sensing and processing operations that result in quicker energy depletion. Small, inexpensive, low-power, multipurpose nodes that are connected to one another form the basis of WSNs. Efficiently gather & communicate data to a washbasin. Cluster Heads (CHs) are used in cluster-based approaches to effectively arrange WSNs for data collection and energy conservation. A CH collects data from cluster nodes and aggregates/compresses it before sending it to a sink. The node's greater responsibility does, however, result in a higher energy drain, which leads to uneven network deterioration. This is made up for by LEACH (Low Energy Adaptive Clustering Hierarchy), which probabilistically alternates CH roles among nodes with energy over a set threshold. CH selection in WSN is NP-Hard because optimal data aggregation with effective energy savings cannot be done in polynomial time. To improve system performance, the synchronous firefly approach, a modified firefly heuristic, is introduced in this paper. A thorough simulation shows that the suggested method performs better than LEACH and energy-efficient hierarchical clustering. In today's world of intelligent networks, the internet of things (IoT) and industrial IoT (IIoT) are extremely important, and they fundamentally use a wireless sensor network (WSN) as a perception layer to collect the necessary data. The difficulty here is the usage of minimal energy for processing and communication. This data is processed as information and sent to cloud servers through a base station. The lifespan of WSNs is increased by the dynamic generation of cluster heads and energy-conscious clustering strategies.