HAO-AVP:用于水下无线传感器网络的透基尼增强学习辅助等级空隙修复协议
Lijun Hao1, Chunbo Ma1, Jun Ao1
1School of Information and Communication Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
混合声学光学自适应空洞处理协议 (HAO-AVP) 通过主动避免路由空洞,提高空洞识别和恢复率以实现可靠的通信来增强水下无线传感器网络 (WSN).
科学领域:
- * * 水下通信方式
- * 无线传感器网络 (WSN) 是一个无线传感器网络.
背景情况:
- *WSN在数据采集方面面临可靠性挑战,原因是由于网络稀疏性,能量分布不均和高动态性造成的路由空白.
- *现有的协议在复杂的水下环境中难以保持稳健的通信.
研究的目的:
- * 提出混合声学光学自适应空隙处理协议 (HAO-AVP) 以实现高度可靠的水下WSN通信.
- * 解决和减轻由网络稀疏性,能量不均和动态性引起的路由空白.
主要方法:
- * 采用基尼系数和来实现强化学习机制,以优化能量分布并主动避免空隙.
- * 开发了一个协作机制,将马尔科夫链能预测模型与按需跳跃发现集成为拓动态.
- * 设计了四个级别的渐进恢复策略 (介质内部调整,跨介质跳跃,路径回溯,AUV辅助恢复) 以实现高效的链路修复.
主要成果:
- *与现有协议相比,HAO-AVP协议在空洞识别率 (7.6%-25.3%) 和空洞恢复率 (4.3%-24.2%) 中显著改善.
- * 该协议表现出增强的稳定性和延长的网络生命周期,特别是在稀疏和动态的水下WSN中.
结论:
- *HAO-AVP协议有效地克服了水下WSNs中的路由空白挑战.
- * 拟议的适应性空隙处理策略显著提高了通信可靠性,稳定性和网络寿命.
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