利用移动 Sink 的无线传感器网络智能节能数据路由方案

Hassan Al-Mahdi, Mohamed Elshrkawey, Shymaa Saad, Safa Abdelaziz
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摘要

数据收集和能源消耗是无线传感器网络(WSN)的关键问题。为了解决这些问题,需要使用聚类和路由算法。因此,本文提出了一种利用移动汇(MS)的 WSN 智能节能数据路由方案,以节约能源并延长网络寿命。所提方案有两种主要运行模式:配置模式和运行模式。在配置模式下,一次应用新颖的聚类机制,并引入预调度簇头(CH)选择,以确保传感器节点(SN)之间能量消耗均匀。调度技术在 WSN 的整个生命周期中为每个簇选择连续的 CH,由基站(BS)管理,以最大限度地减少 SN 的能量消耗。在运行模式下,要实现两个主要目标:每个 CH 以最小的信息开销感知和收集数据,并使用遗传算法为 MS 建立最佳路径。最后,MS 将收集到的数据上传到 BS。我们进行了大量仿真,从稳定期、网络寿命、平均能耗、数据传输延迟、信息开销和吞吐量等方面验证了所提方案的效率。结果表明,所提出的方案明显优于最新的先进方法。利用方差分析进行假设检验和事后分析,通过统计验证证实了上述结果。
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An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink
Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.
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