Coverage optimization in a terrain-aware wireless sensor network

In hostile environments, random deployment of a Wireless ¡Sensor Network (WSN) may be the only viable approach. However, this leads to coverage holes in the Region of Interest (ROI) of the network, which degrades the WSN's quality of service. Hence, there is a need for an algorithm that relocates the sensing nodes to maximize the coverage while minimizing the mobility cost. The cost of mobility is directly related to the traveled distance and the severity of the terrain. Since this problem is NP-complete, this work examines several evolutionary computation techniques in search for an optimal solution. Three algorithms are used to examine this problem: the Artificial Immune System (AIS) algorithm, the Normalized Genetic Algorithm (NGA) and the Particle Swarm Optimization (PSO) algorithm. Multiple experiments are carried out to assess the performance of the utilized algorithms, where depending on the scenario adopted for simulations, some algorithms perform better than the others. In the case where the execution time is not a critical issue, the AIS and NGA algorithms outperform the PSO algorithm in terms of coverage rate and mobility cost, especially for a lower count of sensors.