TSO clustered protocol to extend lifetime of IoT based mobile wireless sensor networks

Giji Kiruba Dasebenezer, Benita Joselin
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引用次数: 2

Abstract

Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.
TSO集群协议延长基于物联网的移动无线传感器网络的生命周期
移动无线传感器网络(MWSNs)的能量利用是近年来各种相关研究工作中的一个重要问题。聚类方法是实现能量利用的最有效方法。簇头(CHs)的确定是mwsn中的一项重要任务,因为它在接收、广播、捕获来自物联网节点的数据并将其广播到基站(BS)时需要消耗巨大的能量。CHs选择不当会消耗能量,从而降低网络的存在性。采用适当的优化方法,建立一个能源资源网络来确定CHs。提出了一种基于虎群优化(TSO)方法的聚类方法,以减少聚类形成和传播阶段的能量消耗。该方法考虑了传感器对CH的簇内距离和传感器的滞留能量等参数。该方法在各种环境、不稳定传感器和CHs中进行了广泛的实验。采用粒子群算法(PSO)、Cat群算法(CSO)和多目标混合遗传算法(MHGA)对该算法进行了评价,并在ns2中对数据传输、延迟、滞留能量进行了仿真。
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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