基于聚类协议和STR算法的Zigbee WDSN性能分析

R. M. Brigitta, P. Samundiswary
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摘要

近年来,无线网络以其易于安装和灵活的特点在网络通信中占有突出的地位。在各种无线领域中,基于Zigbee的无线动态传感器网络(WDSN)可以很好地支持节点移动引起的动态问题。在基于Zigbee的无线动态传感器网络(WDSN)中,主要考虑的是如何有效地利用移动节点之间的能量。在Zigbee WDSN中,使用了各种技术来实现能源效率。其中,节点聚类是最好的方法之一,因为它旨在减少能量消耗,增加网络的寿命。因此,本文通过考虑可移动性节点对采用聚类方案的Zigbee WDSN进行性能分析,并与采用非聚类技术的Zigbee WDSN进行性能比较。提出的工作总结如下:以集群方式部署网络,利用集群协议确定节点的剩余能量。在每个集群中,集群协议选择剩余能量最大的节点作为该集群的头部。然后利用STR算法将感知数据从成员节点路由到簇头。因此,聚类技术和STR通过最短路径路由信息,为提高平均剩余能量和包投递率(PDR)铺平了道路。采用ns2进行仿真,计算分析了非聚类和聚类方法的平均剩余能量和PDR等性能指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance analysis of Zigbee WDSN using clustering protocol and STR algorithm
In recent years, wireless networking plays a prominent role because of its easy installation and flexibility. Among the various wireless domains, Zigbee based Wireless Dynamic Sensor Networks (WDSN) pose a good support to the dynamics that arise when the nodes are induced with mobility. In the Zigbee based Wireless Dynamic Sensor Network (WDSN), major consideration is on utilizing the energy efficiently among the mobile nodes. Various techniques are used to attain energy efficiency in Zigbee WDSN. Among them, clustering the nodes is one of the best methods, since they aim at reducing the energy dissipation and increasing the life span of the network. Hence, in this paper, the performance of Zigbee WDSN using clustering scheme is done by considering the nodes with mobility and compared with performance of Zigbee WDSN using non clustering technique. The proposed work is summarized as follows: the network is deployed as clusters and the remaining energy of the node is determined by utilizing clustering protocol. In each cluster, the clustering protocol opts for the node with the maximum remaining energy as the head of that cluster. Then STR algorithm is utilized to route the sensed data from the member nodes to the cluster head. Hence, the clustering technique and STR, route the information through the shortest path paving the way for enhanced average residual energy and Packet Delivery Ratio (PDR). The simulations are done by using ns2 and performance metrics such as average residual energy and PDR are computed and analyzed for non clustered and clustered method.
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