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首页> 外文期刊>Sustainable Computing >MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks
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MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks

机译：MCH-EOR：无线传感器网络中基于多目标簇头的能量感知优化路由算法

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摘要

Hierarchical Wireless Sensor Networks (WSNs) have got vital application domains in modern era especially in monitoring and tracking of events, and without human intervention. In WSN, sensor nodes are characterized to have short life span due to continuous sensing and consequently the battery drains quickly. Under heavy traffic condition, sensors in close proximity to sink die quickly and initiate energyhole problem. Thus, optimal usage of available energy is a key challenge in WSN assisted applications. A precise clustering and optimal path selection from sensor nodes to sink has become extremely important to preserve energy. Keeping this in view, the paper presents a Multi-Objective Based Clustering and Sailfish Optimizer (SFO) guided routing method to sustain energy efficiency in WSNs. In it the Cluster Head (CH) is selected, based on effective fitness function which is formulated from multiple objectives. It helps to minimize energy consumption and reduces number of dead sensor nodes. After CH selection, SFO is used to select an optimal path to sink node for data transmission. The proposed approach is analytically analyzed and results are compared with the similar existing approaches namely, Grey wolf optimization (GWO), Genetic algorithm (GA), Ant Lion optimization (ALO), and Particle Swarm Optimization (PSO) in terms of energy consumption, throughput, packet delivery ratio, and network lifetime. The simulation results show that proposed method has performed 21.9% and 24.4% better in terms of energy consumption and number of alive sensor nodes respectively when compared to GWO. Further, it shows significantly better results than other optimization-based approaches. (C) 2020 Elsevier Inc. All rights reserved.
机译：分层无线传感器网络（WSNS）在现代时代获得了重要的应用领域，特别是在监控和跟踪事件，而没有人为干预。在WSN中，传感器节点的特征在于由于连续感测而具有短的寿命，因此电池耗尽。在繁忙的交通状况下，快速接近水槽的传感器靠近并开始乐能井问题。因此，可用能量的最佳用法是WSN辅助应用程序中的关键挑战。从传感器节点到沉没的精确聚类和最佳路径选择变得非常重要以保持能量。介绍这篇文章介绍了基于多目标的聚类和帆钓优化器（SFO）引导路由方法，以维持WSN中的能效。在它中，基于从多个目标配制的有效健身功能选择簇头（CH）。它有助于最小化能量消耗并减少死传感器节点的数量。在CH选择之后，SFO用于选择用于数据传输的宿节点的最佳路径。在能量消耗方面，分析了分析了所提出的方法，将结果与类似现有方法相似，灰狼优化（GWO），遗传算法（GA），蚂蚁优化（ALO）和粒子群优化（PSO）。吞吐量，分组传递比和网络生命周期。仿真结果表明，与GWO相比，在能量消耗和活性传感器节点的数量方面，所提出的方法在能量消耗和数量方面进行了21.9％和24.4％。此外，它显示出比其他基于优化的方法更好的结果。 （c）2020 Elsevier Inc.保留所有权利。

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