WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization

In order to optimize the deployment of wireless sensor network nodes, and avoid energy consumption increase due node redundancy uneven coverage, multi-objective mathematical optimization problem area coverage is transformed into a function problem. Aiming at rate, dormancy rate uniformity, idea genetic algorithm mutation introduced based on discrete binary particle swarm global optimal speed mutated falling local solution. further improve ability algorithm, adaptive learning factor inertia weight are obtain nodes. The experimental results show that can reduce number active nodes efficiently, prolong lifetime under premise greater than 90%, compared with an called configuration protocol, finding minimum working sets in networks, optimization-g literature, this reduced by about 36%, 30% 23% respectively.