Abstract
Underwater sensor networks have recently emerged as a promising networking technique for various underwater applications. However, the acoustic routing of underwater sensor networks in the aquatic environment presents challenges in terms of dynamic structure, high rates of energy consumption, long propagation delay, and narrow bandwidth. Therefore, it is difficult to adapt traditional routing protocols, which are known to be reliable in terrestrial wireless networks. In this study, we focus on the development of novel routing algorithms to tackle acoustic transmission problems in underwater sensor networks. The proposed scheme is based on reinforcement learning and game theory and is designed as a routing game model to provide an effective packet-forwarding mechanism. In particular, our Q-learning game paradigm captures the dynamics of the underwater sensor networks system in a decentralized, distributed manner. The results of a performance simulation analysis show that the proposed scheme can outperform existing schemes while displaying balanced system performance in terms of energy efficiency and underwater sensor networks throughput.
Highlights
Over 70% of the earth is covered with water in the form of rivers, canals, seas, and oceans
This article makes the following contributions: (1) we develop a novel routing scheme for underwater sensor networks (USNs); (2) we integrate game theory and the Q-learning algorithm to handle routing decisions; (3) we adopt a distributed online approach to implement self-adaptability and real-time effectiveness; (4) we design a routing algorithm that balances contradictory requirements; and (5) the probabilities for routing decisions are initially determined based on the current USN condition
We provide a brief introduction to the game theory model and general reinforcement learning mechanism, which form the theoretical basis of the proposed USN routing scheme
Summary
Over 70% of the earth is covered with water in the form of rivers, canals, seas, and oceans. The aim of this article is to propose a new routing scheme for USNs by addressing various issues concerning the harsh underwater environment, most notably the energy efficiency, long propagation delay, dynamic topology changes, and node localization of aquatic operations. This article makes the following contributions: (1) we develop a novel routing scheme for USNs; (2) we integrate game theory and the Q-learning algorithm to handle routing decisions; (3) we adopt a distributed online approach to implement self-adaptability and real-time effectiveness; (4) we design a routing algorithm that balances contradictory requirements; and (5) the probabilities for routing decisions are initially determined based on the current USN condition. We present our conclusion and discuss ideas for future work
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