Voronoi, Genetic Algorithms and Their Tandem Application in Wireless Sensor Network Deployment

Abstract

Wireless Sensor Network (WSN) had become almost an indispensible especially the demand for data acquisition from national security to disaster mitigation management, weather data to environmental changes and from many more agencies. The effectiveness and efficacy of WSN dependent on the strength and weakness of the deployment of the sensor nodes which collect and transmit the data. The success of data acquisition in any network depended upon the adequacy of coverage by the sensor nodes; which in turn depended on the method of deployment and redeployment. Since deterministic deployment of nodes could not always be done, random deployment was adopted as a compulsion rather than an option. The random deployment of sensors by nature provided poor network coverage and leading to unsatisfactory data acquisition. Therefore, a better method was sought-after to redeploy the sensors that were deployed earlier at random. Hence, the compelling need had resulted in the development of numerous algorithms for suitably moving the sensors for maximum coverage. Such algorithms were of standalone ones or hybrid/combination in nature. One such combination algorithm termed as Voronoi-Genetic Algorithm (V-GA) a combination/tandom application of Voronoi Vertex Averaging Algorithm (VVAA) and Genetic Algorithm (GA) was analyzed in this study. The displacement and coverage performance were studied, analyzed and compared with that of random deployment and redeployment by the earlier proposed algorithms namely VVAA and GA by the same researcher.