Conditional Sensor Deployment Using Evolutionary Algorithms

Sensor deployment is a critical issue, as it affects the cost and detection capabilities of a wireless sensor network. Although many previous efforts have addressed this issue, most of them assume that the sensing field is an open space. In this work, we consider the sensing field as conditional regions. The Sensor Location Problem (SLP) is a nonlinear nonconvex programming problem which aims to locate sensors to monitor a constrained region. The objective is to determine the locations that will maximize the coverage. Three evolutionary algorithms, particle swarm optimization (PSO), genetic algorithm (GA) and Adaptive Hybrid Optimization (AHO) were used to solve the SLP. Several variants (sensing patterns, sensor counts and region constrains) were tested and results show that the three algorithms are able to obtain good solutions. The proposed AHO proved that it is able to achieve the benefits of both methods and avoid their drawback by smartly switching between them during optimizati...

• Type: Journal
• Year: 2010
• Where: JCIT
• Authors: M. Sami Soliman, Guanzheng Tan
• Keywords: JCIT 2010, Sensor, Sensor Network, Wireless Sensor Network