GWO-LPWSN Grey Wolf Optimization Algorithm for Node

The Internet of Things (IoT) relies heavily on wireless sensor networks (WSNs). The energy resources that sensor nodes can use, however, are constrained by a WSN-based IoT network. By organising nodes into clusters to reduce the transmission distance between sensor nodes and base stations, a clustering protocol offers a reliable technique to ensure node energy savings and enhance network longevity (BS). On the other hand, the clustering mechanism in the existing clustering protocols is flawed, which has a detrimental impact on their efficacy. In order to extend the lifespan of WSN-based IoT devices, we suggest an improved energy-efficient clustering protocol (IEECP) in this study. The planned IEECP is divided into three components. For the overlapping balanced clusters, the ideal number of clusters is first established. A modified fuzzy C-means algorithm is then used to create the balanced-static clusters, coupled with a system to balance and reduce the energy usage of the sensor nodes. Cluster heads (CHs) are picked in the optimal locations by rotating the CH function across cluster members using a new CH selection-rotation algorithm that includes a back-off timing mechanism for CH selection with a rotation mechanism for CH rotation. The suggested method, in particular, balances and minimises energy use. IEECP is ideal for networks with a long lifespan since it reduces and balances node energy consumption by optimising the clustering structure in the proposed protocol. Here, we specifically introduced the grey wolf optimisation (GWO) metaheuristic method. In contrast to the leadership hierarchy and attacking tactics that it follows, this algorithm imitates the social behaviour of grey wolves. Localization is a growing difficulty in wireless sensor networks (WSN). With the aid of WSN anchor nodes, this problem seeks to determine the geographic location of unidentified nodes. In order to solve the node localization problem, the GWO algorithm is used in this study to identify the right position of unknown nodes.

Keywords: Wireless Sensor Network, Internet of Things, Clustering Protocol, Energy Consumption, Network Lifetime.