Developed NSGA-II to Solve Multi Objective Optimization Models in WSNs

Abstract

"Wireless sensor networks (WSNs) are spatially distributed at diverse locations to monitor different physical or environmental conditions". Subject to the sensing part duty, sensors can transmit their data through the network to other nodes or to the base station. The growth of WSN applications was motivated to assist the awkward activities in military, industrial and healthcare applications. Sensors size and cost restrictions add many constraints on its performance such as energy, computational speed, "communications bandwidth" and memory. Most of the real-world engineering optimization problems represent multi-Objective problems. Objectives are often conflicting. Multi-objective optimization (MOO) is the optimization of conflicting objectives. Their solutions are set of answers that describe the best tradeoff between conflicting objectives. In this paper, a developed non-dominated sorting genetic algorithm (NSGA-II) will be proposed to address certain WSN issues. It aims to control the overlapping level between nodes via unit desk graph connectivity model. A suggested Multi-objective optimization model will also help in defining the best tradeoff between network coverage and connectivity as two competing objectives.