Paper Info
Title
Qlaco: Q-Learning Aided Ant Colony Routing Protocol For Underwater Acoustic Sensor Networks
Abstract
Recently, the technology of underwater wireless sensors networks (UWSNs) has received more attention on the exploitation of marine resources. However, underwater acoustic communication is still the only reliable means of ocean communication, which is entirely different from the terrestrial scene. In this paper, we propose Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Moreover, we propose an anti-void mechanism to solve the void region dilemma. Simulation results show that QLACO outperforms Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR) and the depth-based protocol (DBR) in terms of PDR, energy consumption and latency.
Year
DOI
Venue
2020
10.1109/WCNC45663.2020.9120766
2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
Keywords
DocType
ISSN
Routing protocol, Q-Learning, ant colony algorithm, UWSNs
Conference
1525-3511
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Zhengru Fang1152.59
Jingjing Wang213629.50
Chunxiao Jiang32064161.92
Biling Zhang45812.09
Chuan Qin500.68
Yong Ren6104499.99