基于机器学习和群集智能在IIoT中进行能源采集的高效算法
Peizhen Xing1, Hui Zhang2, Morched Derbali3
1Henan Vocational College of Water Conservancy and Environment, Zhengzhou, 450008, Henan, China.
Heliyon
|July 10, 2023
概括
本研究介绍了一种高效的联合学习算法,通过优化资源管理来延长物联网 (IoT) 设备的寿命. 与现有解决方案相比,新方法提高了性能,解决了大规模物联网网络中的电池限制.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 和工业物联网 (IIoT) 正在迅速扩展,成为各种应用的组成部分.
- 电池寿命有限和高维护成本对大规模物联网部署构成重大挑战.
- 有效的资源管理对于维持物联网设备的性能和寿命至关重要.
研究的目的:
- 提出一个高效的基于学习的联合算法,用于IIoT网络中的资源管理.
- 为了应对延长物联网设备寿命的挑战,同时保持最佳性能.
- 为了克服大规模物联网系统中电池限制所带来的局限性.
主要方法:
- 拟议的算法将优化问题分解为子问题.
- 粒子优化 (PSO) 用于管理能源预算.
- 一个代匹配算法用于通信资源优化.
主要成果:
- 与现有方法相比,开发的算法表现出优越的性能.
- 通过公共服务任务实现了有效的能源预算管理.
- 成功实施了优化通信资源配置.
结论:
- 拟议的联合学习算法为IIoT的资源管理提供了有效的解决方案.
- 这种方法有效地延长了物联网设备的寿命,并提高了整体网络性能.
- 这些发现有助于克服大规模物联网网络中的电池限制.
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