Probabilistic Multi-Tiered Grey Wolf Optimizer-Based Routing for Sustainable Sensor Networks

Abstract

Wireless sensor networks (WSN) have a wide range of applications. Therefore, developing an energy-efficient methodology for
estimating cluster heads (CHs) to ensure efficient data transmission has become highly relevant. Meta-heuristic strategies for optimal CHs are the current investigation inclination. As the network grows, the conventional optimization strategies emerge unsuccessful, and the outcomes of hybridizing bring performance enhancement in WSN. A Probabilistic Multi-Tiered Grey Wolf Optimizer (GWO) was
implemented in this study on an upgraded Grey Wolf Optimizer for optimum CH selection. It used fitness value to strengthen GWO’s
search for the best solution, resulting in even dispersal of CHs. Communication routes were updated based on routes to the CHs and
base station to lessen energy consumption by a layered-based routing scheme. GWO’s governance enhanced the network’s ability. The distributed nodes’ geographical territory was categorized into four tiers. CH was chosen grounded on the objective value that required fewer difficult control factors than existing techniques. Simulations showed that the suggested technique could extend the network’s stability time by (31.5 %) compared to hetDEEC-3, L-DDRI, Novel-LEACH-POS, DBSCDS-GWO, and P-SEP.