边缘计算WSN的集群头选择方法基于改进的Sparrow搜索算法
Shaoming Qiu1, Jiancheng Zhao1, Xuecui Zhang2
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了无线传感器网络 (WSN) 的边缘计算系统,以减少能源消耗和延长网络寿命. 拟议的方法有效降低了能源消耗,并延长了运行时间.
科学领域:
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 无线传感器网络 (WSN) 是一种无线传感器网络.
- 边缘计算 边缘计算
背景情况:
- 无线传感器网络 (WSN) 在物联网中至关重要,但由于有限的传感器节点能量而面临限制.
- 在WSN数据传输中,高能耗和短生命周期阻碍了它们的广泛应用.
- 现有的WSN系统难以有效地平衡资源分配和网络寿命.
研究的目的:
- 提出一个双层WSN系统,利用边缘计算来解决能源消耗和生命周期问题.
- 开发适合传感器网络的无线能源消耗和距离优化模型.
- 建立一个优化目标,重点是平衡负载和距离因素,以提高效率.
主要方法:
- 实现一个两层WSN架构,结合边缘计算原则.
- 开发用于优化无线能源消耗和节点距离的数学模型.
- 利用改进的子搜索算法来平衡传感器节点的分布.
主要成果:
- 拟议的边缘计算方法可显著降低整体网络能耗.
- 模拟实验表明,能源使用量显著下降26.8%.
- 优化的节点分布有效地延长了WSN的运行生命周期.
结论:
- 基于边缘计算的双层WSN系统为能源效率提供了一个可行的解决方案.
- 通过优化节点分布来平衡负载和距离因素可以提高网络性能.
- 改进的子搜索算法有助于减少资源消耗和延长网络寿命.
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