Enhancing Coverage and Efficiency in Wireless Sensor Networks Using Bio-Inspired Improved Moth-Flame Optimization (IMFO) Technique

Abstract

WSNs have become highly implemented in several disciplines, ranging from medicine and ecology to agriculture, among many other areas. Critical for development and advancement are innovative approaches towards boosting Wireless Sensor Networks (WSNs) where positioning and density become prime objectives with a goal towards complete sensing within a much more expansive space. This research is interested in finding coverage nodes on a target-based WSN over a wider area by applying the bio-inspired Improved Moth-Flame optimization technique, IMFO. It has been assumed that insects apply transverse orientation for finding their way in the wild. In this work, the technique IMFO targets to enhance the rate of convergence in wireless sensor networks by maximizing coverage with a minimum number of nodes needed across a broader region and with low compilation time. The whole sensing area is partitioned into an exact number, "n," of individual sensing zones. The sensors were localized in each location using IMFO, and for the next iterations, the entire localized sensors of all regions were treated as a moth.  A great deal of efficacy and efficiency is shown by the results of the trial. The proposed approach entails modeling the optimization problem mathematically, taking into account variables like energy consumption, communication range, sensing coverage, and total node count. Whenever there comes an optimization issue, the toolbox in MATLAB optimizes with a powerful optimization engine and comes up with the best possible solution. Several performance measures include compilation time, F-value, packet delivery ratio, and throughput, which are significantly improved according to the proposed method.