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Efficient Opportunistic Routing in UASNs Using Dual-Time-Scale Learning: Fast LNB-2 and Slow Q-Learning
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DOI：10.1109/tgcn.2026.3707821.png)
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Abstract
En 中文
In underwater acoustic sensor networks (UASNs), traditional greedy forwarding is prone to routing void regions (RVRs) under time-varying acoustic links, intermittent connectivity, and unfavorable local topology, which degrades reliability and causes ineffective forwarding. To address this issue, this paper proposes LQ-T2OR, an efficient opportunistic routing protocol based on dual-time-scale learning. On the fast time scale, LNB-2 estimates the non-RVR forwarding capability of candidate relays using two-hop common-neighbor evidence. On the slow time scale, Q-learning optimizes long-term relay selection through a reward function that incorporates energy consumption, energy balance, forwarding distance, delay, and RVR risk. A distributed holding-time mechanism is further adopted to suppress redundant forwarding. Simulation results show that LQ-T2OR improves delivery reliability and routing robustness while maintaining competitive delay and energy-related performance.
Keywords:
Underwater acoustic sensor networks (UASNs)
RVR
opportunistic routing
LNB-2
Q-learning
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1.3K
Citations:
4.3K
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