改进的基于簇的路由算法在无线传感器网络中的实现

P. Satyanarayana, Sa P. Teja Venkata, P. R. Kumar, Varma S. Girish Kumar, M.D. Zulfath Aamina
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引用次数: 2

摘要

由传感器节点组成的网络称为无线传感器网络。在该网络中,传感器节点位于地理位置或相关区域,用于监测各自的物理状况。环境传感、卫生保健监测、边境监测、森林监测是无线传感器网络的几个应用领域。关键的挑战将是能源利用。为了克服这一挑战,已经提出了许多算法。这些算法都是基于聚类的算法,为能源利用问题提供了一个答案。聚类算法的工作原理解释如下。最初,该算法将网络划分为单元,即集群。然后引入遗传算法,确定网络的最优节点数。然后将这些节点放置在环境中,将染色体长度设置为与节点数相等,使其收敛速度较慢。因此,染色体长度会减少,因此我们可以通过快速收敛达到最优解。相反,K-Means算法在每条染色体上设置簇头后使用,这些簇头被委托为算法的早期点,用于快速聚类过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of modified Cluster Based Routing Algorithm to Enhance QoS for Wireless Sensor Networks
A Network consisting of sensor nodes is referred to as Wireless Sensor Network. In this Network the sensor nodes are positioned in geographical locations or concerned regions for monitoring of respective physical conditions. Environmental sensing, health care surveillance, border surveillance, forest monitoring are few of the applied fields of wireless sensor networks. The key challenge will be the energy utilization. To overcome this challenge numerous algorithms have been proposed. These various algorithms proposed are cluster-based algorithms which contribute an answer for the energy utilization problem. The working of clustering algorithm is explained as follows. Initially, the algorithm divides the network into cells say clusters. Then, the genetic algorithm comes into picture so that the optimal numbers of nodes are determined in a network. Then these nodes are placed in the environment, the chromosome length is set equal to number of nodes so that it may have slow convergence. Due to this there will be reduction in the chromosome length and so that we can reach the optimal solution due to swift convergence. Conversely, K-Means algorithm is used after setting up the cluster heads in each chromosome, those are delegated as the early points for the algorithm which is used for speed clustering procedure.
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