首页> 外文期刊>International journal of communication systems >EE-hHHSS: Energy-efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk-salp swarm optimization algorithm
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EE-hHHSS: Energy-efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk-salp swarm optimization algorithm

机译:EE-HHHSS:使用Hybrid Harris Hawk-Salp群综合算法的高节能无线传感器网络

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Wireless sensor network (WSN) includes power-efficient sensor nodes to convey information to the base station (BS). This network comprises a number of sensor nodes that perform sensing, processing, and wireless communication abilities to monitor a specified sensing field. Therefore, it is necessary to prolong WSN lifetime using energy efficient optimization methods, because the sensor nodes are operated by battery. Also, it is difficult to replace the battery of the nodes located in harsh environments. Thus, energy-efficient routing is a crucial phenomenon in WSN. In this article, energy-efficient WSN with mobile sink (MS) strategy is proposed using hybrid Harris hawk and salp swarm (Hybrid HH-SS) optimization algorithm. In order to achieve energy efficiency, finding an optimal route for MS is a critical task. The MS discovers an optimal path to interconnect with the cluster heads (CHs) by adaptive ant colony optimization (AACO) algorithm. Hence, the proposed hybrid algorithm minimizes the energy consumption (EC), packet loss rate (PLR), and end-to-end (E2E) delay and enhances the lifetime of the network. The proposed work is implemented in JAVA platform, and simulation outcomes show that the proposed approach enhances the wireless sensor network performances. Simulation results show enhancement in energy efficiency in terms of network lifetime, packet delivery rate, average throughput, packet loss rate, energy efficiency, and end-to-end delay.
机译:无线传感器网络(WSN)包括高功率传感器节点,以将信息传送到基站(BS)。该网络包括许多传感器节点,其执行传感,处理和无线通信能力来监视指定的感测字段。因此,有必要使用节能优化方法延长WSN寿命,因为传感器节点由电池操作。此外,难以更换位于恶劣环境中的节点的电池。因此,节能路由是WSN中至关重要的现象。在本文中,使用Hybrid Harris Hawk和SALP Swarm(HH-SS)优化算法提出了具有移动水槽(MS)策略的节能WSN。为了实现能效,找到MS的最佳路线是一个关键任务。 MS发现通过自适应蚁群优化(AACO)算法与簇头(CHS)互连的最佳路径。因此,所提出的混合算法最小化能量消耗(EC),分组丢失率(PLR)和端到端(E2E)延迟并增强网络的寿命。拟议的工作是在Java平台中实施的,仿真结果表明,所提出的方法增强了无线传感器网络性能。仿真结果表明,在网络寿命,数据包传递率,平均吞吐量,丢包率,能效和端到端延迟方面提高了能源效率。

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