基于改进粒子群算法的红外WSN覆盖研究

Research on infrared WSN coverage based on improved particle swarm optimization

为了提高红外无线传感器网络覆盖效果,采用改进粒子群算法。首先建立红外无线传感器网络覆盖模型,包括无线传感器的覆盖率、剩余能量率、利用率;接着对粒子进行方向感知,粒子的适应度函数值与自身当前最佳值比较,获得不同的更新速度与位置,为了避免进化过程中受方向感知粒子过度引导,自适应感知因子在算法运行初期设置较大值,而在算法运行后期设置较小值;最后构建粒子群适应度函数,给出了算法流程。实验显示本文算法增加了覆盖率,减少了功耗,同时也降低了传感器节点利用率,不同场景下本文算法评价指标较优。

To improve the coverage of infrared wireless sensor network, an improved particle swarm optimization algorithm is adopted.Firstly, the coverage model of infrared wireless sensor network is established, including the coverage rate, residual energy rate and utilization rate of wireless sensor network.Secondly, particles are direction-aware, and the fitness function value of particles are compared with its current optimal value to obtain different update speed and position, the adaptive sensing factor is set to a larger value at the beginning and a smaller value at the end stage, avoiding excessive guidance of direction sensing particles in the evolution process.Finally, the fitness function of particle swarm optimization is constructed and the process is given.The experimental results show that the improved particle swarm optimization not only increases coverage and reduces power consumption but also decreases the utilization of sensor nodes, and the evaluation index is better in different scenarios.