Grey wolf optimization with fuzzy logic for energy‐efficient communication in wireless sensor network‐based Internet of Things scenario

Abstract

Summary Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime.

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

“…There have been many energy-efficient clustering protocols based on the GWO algorithm proposed in the recent times towards optimal CH selection [16,17]. GWO-based methods are proposed for energy optimizations in WSNs by finding the optimal positions of SNs to achieve maximum connectivity and coverage.…”