Wireless Sensor Network Deployment Optimization Using Reference-Point-Based Non-dominated Sorting Approach (NSGA-III)

Abstract

Wireless sensor networks (WSN) nowadays have gained more interest, pushed by the growing necessity for data collection and transmission from both civilian and military domains. WSN is constructed from interconnected sensors and limited resource (battery), which requests great importance on the deployment strategy to increase the performance metrics for WSN (lifetime, coverage, QoS connectivity). Also, the deployment is considered as a fundamental issue in (WSNs) design, and it was taken from the perspective of the multi-objective optimization problem. Many of the existing deployment strategies are based on metaheuristics algorithms such as Genetic Algorithms (GAs) to resolve the deployment problem. In this article, we use and adopt one of the most attractive approaches for wireless sensor networks deployment (WSND) optimization based on metaheuristic searching which is named Non-dominated Sorting Genetic Algorithm-III (NSGA-III) In order to reach maximum coverage and minimize the consumption of energy to maximize the network lifetime under the connectivity constraint. The comparison results have proved the NSGA-III algorithm outperformed the Constrained Pareto-based Multi-objective Evolutionary Approach (CPMEA) that taken as a benchmark for this study. Those results encourage the application of NSGA-III to real-world deployment problems, and the importance of this approach is that it can be handled many objectives.