粒子群优化的无线传感器网络仿真研究

Simulation Research of Wireless Sensor Netwok Based on article Swarm Optimization

研究优化无线传感器问题,针对延长传感器网络的寿命,保证簇的平均分布,提高簇的负载均衡,从而减少能量消耗。传统算法在确定簇首过程中由于忽略了邻居节点的状态信息,容易导致簇内节点过早的出现盲节点现象,从而降低网络的生存时间。要解决上述问题,延长网络生命周期和有效降低能耗,提出一种粒子群优化的无线传感器分簇算法。在充分考虑了簇内邻居节点的能量和距离分布信息的前提下,通过粒子群优化分簇和簇首选择,并进行仿真。仿真结果表明,与LEACH算法相比,算法能有效地均衡网络节点的能量消耗和显著地延长网络寿命,并有效地避免了盲节点现象的过早发生。

In the cluster-based routing protocols,it depends on the reasonable selection of cluster heads to prolong the life cycle of the sensor networks. But the existing algorithms are prone to lead the nodes in clusters to die early due to ignoring the state of neighbors in the process of cluster-heads decision. A new cluster-based algorithm using Particle swarm optimization is proposed to optimize clustering process. The election of cluster-heads needs synthetically consider the state information including location and energy reserved about candidates and their neighbors. The results show that particle swarm optimization outperforms LEACH because of significantly prolonging the networks lifetime,efficiently balancing the networksenergy dissipation,and efficiently delaying the occurrence time of dead nodes.