混合GA-PSO优化用于控制器放置在大规模智能城市物联网网络中的控制器.
Sheeraz Ali Memon1, Darius Andriukaitis1, Dangirutis Navikas1
1Department of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentu g. 50-438, LT-51368 Kaunas, Lithuania.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了一种混合优化算法,将遗传算法 (GA) 和粒子群优化 (PSO) 结合起来,以在智能城市网络中高效地放置控制器. 混合方法显著提高了网络性能,负载平衡和冗余性.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 智慧城市技术 智慧城市技术
背景情况:
- 物联网 (IoT) 是智能城市发展的组成部分.
- 最佳的控制器放置对于物联网网络的性能和弹性至关重要.
- 现有的安置策略可能无法充分解决大规模智能城市网络的复杂性.
研究的目的:
- 为物联网网络中战略控制器放置提出混合优化方法.
- 提高网络性能指标,包括延迟,负载平衡,能源效率和冗余性.
- 在现实世界智能城市模型中验证拟议的算法.
主要方法:
- 开发了一种混合优化算法,将遗传算法 (GA) 和粒子群优化 (PSO) 结合起来.
- 进行了一次大规模的窄带物联网 (NB-IoT) 网络模拟,使用了2000个节点.
- 控制器的放置被优化以最大限度地减少延迟和平衡负载,专注于能源效率和冗余性.
主要成果:
- 混合GA-PSO算法与随机和K-Means集群放置相比,表现优越.
- 在负载平衡,减少数据包丢失和提高能源效率方面观察到显著的改进.
- 该算法在正常运行,节点故障和流量峰值场景中被证明是强大的和有效的.
结论:
- 拟议的混合GA-PSO算法为优化智能城市物联网网络中控制器放置提供了有效的解决方案.
- 这种方法提高了网络的弹性,可扩展性和整体性能.
- 这些发现强调了该算法在智能城市基础设施中的实际实施潜力.
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