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相关实验视频

Updated: Jun 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在灾难场景中,生物灵感的节能集群基于物联网的路由协议用于物联网.

Shakil Ahmed1, Md Akbar Hossain2, Peter Han Joo Chong3

  • 1Department of Mechanical and Electrical Engineering, Massey University, Palmerston North 4442, New Zealand.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了一种混合蝶优化算法 (BOA) 和粒子群集优化 (PSO) 用于物联网 (IoT) 网络在灾难场景中. 该算法通过优化集群和路由来提高能源效率和网络寿命.

关键词:
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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 设备对于环境传感和减少灾害影响至关重要,但由于电池驱动的传感器,它们面临能源限制.
  • 高效的能源消耗,特别是在容易发生灾害的地区数据传输期间,对于延长物联网网络的运行寿命至关重要.
  • 基于集群的通信是减少节点能量耗尽和提高网络寿命的关键策略.

研究的目的:

  • 开发和评估一种新的混合生物灵感算法,以优化物联网网络中的能源效率和网络寿命,用于灾害管理.
  • 解决现有的集群和路由协议的局限性,包括灾难相关的参数,如剩余能量,沉没距离和网络覆盖范围.

主要方法:

  • 提出了一种混合蝶优化算法 (BOA) 用于集群和粒子优化 (PSO) 用于物联网网络中的路由.
  • 集成的关键灾难场景参数:节点剩余能量,距离水槽的距离和网络覆盖范围.
  • 将拟议的BOA-PSO算法的性能与基准协议 (LEACH,DEEC,PSO,PSO-GA,PSO-HAS) 的性能进行了比较,使用剩余能量,吞吐量和网络寿命作为指标.

主要成果:

  • 该BOA-PSO算法证明了显著的残留能量节约,在短距离场景中显示了17%以上的改善,在长距离场景中显示了10%的改善.
  • 实现了实质性的吞吐量提升:比LEACH提高60%,比DEEC提高53%,比公共服务任务提高37%.
  • 与LEACH和DEEC相比,减少了60%的数据包下降,与PSO相比减少了30%,同时将整个网络寿命提高了10-20%.

结论:

  • 混合BOA-PSO算法为灾难管理中的物联网应用提供了卓越的能源效率和延长的网络寿命.
  • 拟议的方法通过考虑与灾难环境相关的关键参数,有效地优化聚类和路由.
  • 这项研究为提高物联网网络在关键灾害响应行动期间的可靠性和性能提供了强大的解决方案.