PSO based Deployment Algorithms in Hybrid Sensor Networks

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is supported by the several methods. Particle Swarm Optimization (PSO) algorithm is one these methods, however computation time required is a big bottleneck. This paper proposes three dynamic PSO-based deployment algorithms that reduce the computation time. First algorithm by the name of "PSO-LA" algorithm comprised of PSO algorithm and learning automata. In this algorithm, speed of particles is corrected by using the existing knowledge and the feedback from the actual implementation of the algorithm. Hence in this algorithm mobile nodes move more objectively than PSO and achieve the result with less number of repetitions. To improve performance of this algorithm, second algorithm by the name of "Improved PSO-LA" algorithm is introduced, regulating its movement without an impact from the movement of other mobile nodes and based on the result gained from its previous movement. The first and second algorithms require sensors to move iteratively, eventually reaching the final destination. In the third algorithm by the name of "Improved PSO-LA with logical movement" with the same round-by-round procedure of the second algorithm, sensors calculate their target locations, virtually move there. The real movement only happens at the last round after final destinations are determined. Simulation results show the effectiveness of our proposed algorithms against other common approaches like VF and PSO algorithms.