基于自适应遗传算法的WSNs分簇路由方法  

An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks

作　　者：陈磊[1] 张颖[1] CHEN Lei;ZHANG Ying(College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学,信息工程学院,上海201306

出　　处：《微电子学与计算机》2020年第11期41-47,共7页Microelectronics & Computer

摘　　要：在设计和部署无线传感网络的过程中,由于部署环境多变,节点能量有限,各节点传输数据时负载不均衡.因此,如何充分利用有限的能量来延长网络寿命,提高数据传输效率和算法的实时性成为了亟需解决的问题.本文针对上述问题提出了一种基于改进型自适应遗传算法的无线传感网络分簇路由协议IAGA-C(Improved Adaptive Genetic Algorithm-Clustering).该方法在分簇过程中,综合考虑了各节点与簇头节点间、簇头节点与基站间的距离以及各节点剩余能量三方面因素来进行簇头的选择。此外,为了提高分簇算法的实时性,本文改进了经典遗传算法的交叉和变异机制,在保证算法有效性的同时缩短了分簇过程所消耗的时间和能量。仿真结果表明,相比于其他协议,该方法在网络寿命、数据传输效率以及实时性等方面有较好的表现.In the process of designing and deploying wireless sensor networks(WSNs),due to the changeable deployment environment and limited energy,the load of each node is unbalanced when transmitting data.Therefore,how to make full use of the limited energy to prolong the lifetime of the network,improve the data transmission efficiency and real-time performance of the algorithm has become an urgent problem to be solved.An improved adaptive-genetic-algorithm based clustering protocol(IAGA-C)for WSNs is proposed in this paper.The cluster heads are selected by integrating the factors of the distance between each node and cluster heads,the distance between cluster heads and base station(BS),and the remaining energy of each node.Additionally,in order to improve the real-time performance of clustering algorithm,this paper improves the crossover and mutation mechanism of classical genetic algorithm,and reduces the time consumed in clustering process while ensuring the effectiveness of the algorithm.Simulation results show that,compared with another clustering routing protocols,the proposed method has good performance on improving network life,data transmission efficiency and real-time capability.

关 键 词：无线传感网络 分簇 自适应遗传算法 网络寿命 数据传输效率 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]