Particle swarm optimization schemes based on consensus for wireless sensor networks

In this paper, we consider sensors that move according to the well-known Particle Swarm Optimization (PSO) scheme in order to improve network coverage. Unlike the original PSO, particle speed is updated by considering a consensus algorithm based on local optimum position. Two different versions of the algorithm have been simulated: a global version that allows nodes to use information of the whole sensor field and a local version based only on neighborhood information. The algorithm based on global information is used as comparison term for the local version. Also, a variant of these algorithms has been implemented by adding the concept of pioneers, which are powerful sensors that explore the field to detect interesting areas before the other sensors become active. In order to evaluate the performance of our schemes, different scenarios have been introduced by varying the probability areas for events to occur in. The performance of the network has been evaluated in terms of coverage and energy consumption for movement and has shown that the proposed techniques obtain remarkable results for both parameters considered.