Optimization of multi-task nodes in wireless sensor networks based on discrete particle swarm algorithm
Xiuying Zhu · Journal of Intelligent & Fuzzy Systems · 2021
Aiming at the competition conflict problem of task allocation of sensor node in wireless sensor network multi-target tracking, a discrete particle swarm optimization tracking task allocation optimization algorithm based on nearest neighbor is proposed. By constructing the mathematical model and objective function of the multi-objective multi-sensor node alliance cooperative tracking task allocation problem, the nearest neighbor method is used to initialize the particle group node task allocation, the objective function is used as the fitness function to guide the particle flight, and the optimal node allocation can be quickly realized. Experiments show that in the case of sparse node coverage, the particle swarm optimization node task allocation method has greatly reduced energy consumption compared with the nearest neighbor method, and can effectively solve the problem of multi-target tracking node task allocation conflict and multiple monitoring alliances on sensor resources the problem of increased system energy consumption during competition conflicts. Discrete particle swarm optimization has superiority for wireless sensor network multi-target tracking in actual environment.