为高效的6G物联网流量管理和多路由策略提供混合优化
J Logeshwaran1, Shobhit K Patel2, Om Prakash Kumar3
1Department of Computer Science, Christ University, Bengaluru, Karnataka, 560029, India.
Scientific reports
|December 27, 2024
概括
这项研究引入了一个新的框架,用于在6G网络中监测智慧城市空气污染. 它通过使用量子启发的算法和深度强化学习来提高物联网 (IoT) 的效率和网络稳定性.
科学领域:
- 计算机科学 计算机科学
- 电信工程 电信工程 电信工程
- 环境监测 环境监测
背景情况:
- 物联网 (IoT) 设备的扩散在6G通信系统中带来了重大的流量管理挑战.
- 智能城市可靠的空气污染监测需要可扩展和准确的数据收集框架.
研究的目的:
- 开发一个全面的框架,以利用6G能力,在智能城市中可靠监测空气污染.
- 通过先进的技术来提高物联网数据的准确性,覆盖范围和可扩展性.
主要方法:
- 拟议的量子启发的集群算法 (QCA) 和量子纠和移动度量 (MoM) 以实现高效的集群.
- 使用深度强化学习 (DRL) 实现动态集群头 (CH) 选择,以实现网络可持续性.
- 使用混合量子遗传算法和殖民地优化 (QGA-ACO) 进行数据路由.
主要成果:
- 实现了95%的部署覆盖率,证明了广泛的网络覆盖范围.
- 获得了0.97的集群稳定性指数,表明网络性能强.
- 证明了95%的CH选择效率,优于传统方法.
结论:
- 拟议的框架有效地解决了6G通信系统中的物联网流量和多路由挑战.
- 量子灵感算法和DRL的整合确保了智能城市应用程序的强大和可扩展的物联网生态系统.
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