GPU-Based Parallel Genetic Algorithm for Increasing the Coverage of WSNs

Abstract

Advances in wireless communication, digital systems and micro-electronic-mechanical system technologies led to the development of wireless sensor networks (WSNs) which are used in various critical real-world applications. The fact that WSNs are low cost and eliminate the need for infrastructure led to their replacing traditional networks in area/event monitoring and tracking applications. WSNs consist of small and resource-limited sensor nodes, due to which several problems arise in the WSN development process. One of these problems is coverage. Providing the best coverage with a minimum number of sensor nodes is an NP-hard problem known as the maximum coverage sensor deployment problem (MCSDP). Genetic Algorithms (GAs) have been proved effective in solving optimization problems in many different disciplines (increasing coverage in WSNs, image processing, route planning, etc.). In this study, a GPU-based parallel GA solution for increasing the coverage of a given homogeneous WSN topology in a 2-D Euclidean area is proposed which is the first time this technique is used and parallelized on GPUs to the best of our knowledge. Finally, performance results of the proposed algorithm are compared to the previous work with the emphasis on the achieved performance improvement.