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为了改善集成RFID与WSNs网络中智能节点随机部署时的不合理分布,提高同时读取多个标签信息的能力,提出了基于混沌粒子群(CPSO)的集成网络优化算法,用于寻找智能节点的最佳位置。该最佳位置不仅要保证给定智能节点对标签的最大覆盖率,而且要使得集成网络分布合理。混沌粒子群算法利用了混沌运动遍历性、随机性等特点,对传统粒子群算法进行改进,摆脱了粒子群算法后期易陷入局部极值点的缺点,并保持了前期搜索的快速性。仿真结果表明,该算法比基本粒子群算法具有更好的优化效果,在保证智能节点有较高读取率的同时,也优化了集成网络资源的分布。
In order to improve the irrational distribution of intelligent nodes in integrated RFID and WSNs network and improve the ability to read multiple tags simultaneously, an integrated network optimization algorithm based on Chaos Particle Swarm Optimization (CPSO) is proposed to find intelligent nodes Best location. The best location not only guarantees the maximum coverage of a given smart node for the tag, but also makes the integrated network decentralized. The chaotic particle swarm optimization (PSO) utilizes the characteristics of ergodicity and randomness of chaotic motions to improve the traditional particle swarm optimization algorithm, and overcomes the shortcomings that the particle swarm optimization algorithm easily plunges into the local extremum point in the later period and keeps the rapidity of previous search. The simulation results show that this algorithm has a better optimization effect than the basic particle swarm optimization algorithm. While ensuring the high read rate of intelligent nodes, it also optimizes the distribution of integrated network resources.