Energy Balanced Clustering and Data Gathering for Large-Scale Wireless Sensor Networks

B. Mamalis, Marios Perlitis
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引用次数: 3

Abstract

Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper a two-level clustering approach is presented, combining a traditional gradient-based clustering technique with an evolutionary optimization technique based on the Gravitational Search Algorithm (GSA), and targeting to improved performance in large-scale WSNs (where typical approaches usually lead to performance degradation). The proposed protocol initially creates energy-balanced multi-hop clusters, where the energy of the sensors increases progressively as getting closer to the cluster head (CH). In the second phase of the protocol an appropriate GSA-based evolutionary algorithm is executed in order to assign groups of CHs to specific 'gateways' for the final data forwarding to the base station (BS). The GSA fitness function is adequately defined taking in account both the distance from the CHs to the gateways and the BS as well as the residual energy of the gateways. Simulation results show the high performance of the proposed scheme as well as its superiority over the native GSA-based approach presented in the literature.
大规模无线传感器网络的能量均衡聚类与数据采集
聚类是一种有效的无线传感器网络节能技术。本文提出了一种两级聚类方法,将传统的基于梯度的聚类技术与基于引力搜索算法(GSA)的进化优化技术相结合,以提高大规模WSNs的性能为目标(典型方法通常会导致性能下降)。该协议首先建立了能量平衡的多跳集群,其中传感器的能量随着越来越接近簇头(CH)而逐渐增加。在协议的第二阶段,执行适当的基于gsa的进化算法,以便将CHs组分配到特定的“网关”,以便最终将数据转发到基站(BS)。考虑到CHs到网关和BS的距离以及网关的剩余能量,充分定义了GSA适应度函数。仿真结果表明,该方案具有较高的性能,并且优于文献中提出的基于gsa的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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