Reinforcement Learning-Based Opportunistic Routing Protocol using Depth Information for Energy-efficient Underwater Wireless Sensor Networks

An efficient routing protocol is critical for the data transmission of Underwater Wireless Sensor Networks (UWSNs). Aiming to the problem of void region in UWSNs, this paper proposes a reinforcement learning-based opportunistic routing protocol (DROR). By considering the limited energy and underwate...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 23; no. 15; p. 1
Main Authors: Wang, Chao, Shen, Xiaohong, Wang, Haiyan, Zhang, Hongwei, Mei, Haodi
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Data transmission
opportunistic routing
Q learning
Routing (telecommunications)
routing protocol
Underwater
underwater wireless sensor networks
void region
Wireless sensor networks
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:An efficient routing protocol is critical for the data transmission of Underwater Wireless Sensor Networks (UWSNs). Aiming to the problem of void region in UWSNs, this paper proposes a reinforcement learning-based opportunistic routing protocol (DROR). By considering the limited energy and underwater environment, DROR is a receiver-based routing protocol, and combines reinforcement learning with opportunistic routing to ensure real-time performance of data transmission as well as energy efficiency. To ensure reliable transmission when encountering void regions, a void recovery mechanism is designed to enable packets to bypass void nodes and continue forwarding. Furthermore, a relative Q-based dynamic scheduling strategy is proposed to ensure that packets can efficiently forward along the global optimal routing path. Simulation results show that the proposed protocol performs well in terms of end-to-end delay, reliability, and energy efficiency in UWSNs.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3285751