一种适应性覆盖方法,用于动态无线传感器网络部署,使用深度增强学习
Peng Zhou1,2, Mingqi Kan1, Wei Chen1
1School of Information Science and Engineering, Xinjiang College of Science & Technology, Korla, 841000, Xinjiang, China.
Scientific reports
|August 19, 2025
概括
基于深度增强学习 (ACDRL) 的自适应覆盖意识部署优化了无线传感器网络 (WSN) 的覆盖范围和能源效率. 这种新的方法提高了复杂环境中的网络寿命和监控保真度.
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:
- 覆盖率优化对于无线传感器网络 (WSN) 至关重要,但由于能源限制和复杂的环境,具有挑战性.
- 传统的部署方法难以平衡覆盖质量和能源效率.
- 有限的能源预算需要最大限度地提高网络寿命,同时保持足够的监控能力.
研究的目的:
- 引入基于深度强化学习 (ACDRL) 的自适应覆盖意识部署,用于智能WSN节点放置.
- 解决 WSN 中覆盖率优化和能源平衡的双重挑战.
- 在复杂的场景中开发一种用于自我优化节点放置的新策略.
主要方法:
- 实施深度强化学习框架.
- 整合一个多目标奖励机制.
- 利用层次状态表示来进行自适应部署.
主要成果:
- 与最先进的方法相比,ACDRL显示出更高的覆盖率.
- 拟议的战略大大延长了WSN的运营寿命.
- ACDRL显示出增强的适应性,特别是在高密度部署场景中.
结论:
- ACDRL有效地优化了WSN中的覆盖范围和能源效率.
- 深度强化学习为智能WSN部署提供了一个强大的范式.
- 该框架为复杂和动态的WSN环境提供了强大的解决方案.
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