scholarly journals A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1660 ◽  
Author(s):  
Zhigang Jin ◽  
Yingying Ma ◽  
Yishan Su ◽  
Shuo Li ◽  
Xiaomei Fu
Keyword(s):  
Sensor Networks ◽  
Routing Algorithm ◽  
Underwater Sensor Networks ◽  
Q Learning

Void hole avoidance routing algorithm for underwater sensor networks

2020 ◽  
Vol 14 (21) ◽  
pp. 3837-3844 ◽  
Author(s):  
Kamal Kumar Gola ◽  
Manish Dhingra ◽  
Bhumika Gupta
Keyword(s):  
Sensor Networks ◽  
Routing Algorithm ◽  
Underwater Sensor Networks

scholarly journals A better-performing Q-learning game-theoretic distributed routing for underwater wireless sensor networks

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875472 ◽  
Author(s):  
Sungwook Kim
Keyword(s):  
Sensor Networks ◽  
Simulation Analysis ◽  
Dynamic Structure ◽  
Propagation Delay ◽  
Underwater Sensor Networks ◽  
Performance Simulation ◽  
Q Learning ◽  
Transmission Problems ◽  
Learning Game ◽  
Game Theoretic

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.

scholarly journals Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 992 ◽  
Author(s):  
Huicheol Shin ◽  
Yongjae Kim ◽  
Seungjae Baek ◽  
Yujae Song
Keyword(s):  
Sensor Networks ◽  
Network Throughput ◽  
Underwater Sensor Networks ◽  
Channel Access ◽  
Q Learning ◽  
Acoustic Sensor Networks ◽  
Access Problem ◽  
Markov Decision ◽  
Dynamic Channel ◽  
Physical Features

In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose objective is to maximize the total network throughput without coordinating with or exchanging messages among different underwater sensors. We then propose a distributed deep Q-learning-based algorithm that enables each underwater sensor to learn not only the behaviors (i.e., actions) of other sensors, but also the physical features (e.g., channel error probability) of its available acoustic channels, in order to maximize the network throughput. We conduct extensive numerical evaluations and verify that the performance of the proposed algorithm is similar to or even better than the performance of baseline algorithms, even when implemented in a distributed manner.

Sign in / Sign up

Export Citation Format

Share Document