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Q-Learning-Based Routing Optimization Algorithm for Underwater Sensor Networks
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DOI：10.1109/JIOT.2024.3398797.png)
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Abstract
En 中文
Underwater wireless sensor network (UWSN) plays a vital role in the field of ocean development and exploration. Designing a routing protocol for UWSN is a great challenge due to the characteristics of short lifetime and high delay. This article proposes a Q-learning-based routing optimization algorithm for UWSN. Two reward functions are designed based on the average residual energy of network, integrating factors, such as energy information, transmission delay, and link success rate to better balance transmission quality and lifetime. In addition, a holding time mechanism for packet forwarding is developed according to the priority of nodes. The simulation results show that compared to the DBR and QLFR algorithms, this algorithm can effectively reduce transmission delay and prolong network lifetime.
Keywords:
Routing
Q-learning
Internet of Things
Energy consumption
Routing protocols
Relays
Heuristic algorithms
reinforcement learning (RL)
routing selection
underwater wireless sensor network (UWSN)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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