Dynamic Deployment of Sensors using Virtual Forces-Directed Co-Evolutionary Particle Swarm Optimization (VFCPSO) Method

Wireless sensor networks (WSN) present numerous research possibilities due to the bright and promising future of information technology. WSNs are a group of inexpensive, less power consuming, having multiple functions and tiny wireless nodes that work in unison. They sense the environment; do simple tasks like processing of data and are able to communicate wirelessly over a short distance. How effective a WSN will depend on the probability of coverage and on detecting target. Many coverage strategies like force based (Virtual Forces Algorithm), Grid-Based (triangular lattice, hexagon and square grid) and computational geometry based (Voronoi and Delaunay triangulation) are suggested. Another method is the Particle Swarm Optimization method (PSO). This is a type of Evolutionary Algorithm and shows prospects in being able to solve complex optimization problems. The demand to achieve optimum coverage inspires us to move towards hybrid algorithms. The hybrid algorithms combine more than one of the above mentioned approaches. We aspire to achieve optimum Coverage by implementing an algorithm called Virtual Force-directed Co-evolutionary Particle Swarm Optimization (VFCPSO). This algorithm is a hybrid of Virtual Forces Algorithm and Co-evolutionary PSO. It is flexible enough for a network formed from permutations of homogeneous, heterogeneous, stationary and mobile sensors. The VFCPSO predicts deploying actively with better abilities to search overall and to converge reginally. We aim to implement and compare the results of VFA, PSO, VFPSO and VFCPSO. The expectation here is to get a noticeable increase in effective coverage area and a noticeable decrease in average computation time.