Research Article
QL-EEBDG: QLearning based energy balanced routing in underwater sensor networks
@ARTICLE{10.4108/eai.10-4-2018.154459, author={Obaida Abdul Karim and Nadeem Javaid and Arshad Sher and Zahid Wadud and Sheeraz Ahmed}, title={QL-EEBDG: QLearning based energy balanced routing in underwater sensor networks}, journal={EAI Endorsed Transactions on Energy Web and Information Technologies}, volume={5}, number={17}, publisher={EAI}, journal_a={EW}, year={2018}, month={4}, keywords={Energy tax, network stability period, throughput}, doi={10.4108/eai.10-4-2018.154459} }- Obaida Abdul Karim
Nadeem Javaid
Arshad Sher
Zahid Wadud
Sheeraz Ahmed
Year: 2018
QL-EEBDG: QLearning based energy balanced routing in underwater sensor networks
EW
EAI
DOI: 10.4108/eai.10-4-2018.154459
Abstract
In this paper, we propose a Q-Learning based efficient and balanced energy consumption data gathering routing protocol (QLEEBDG) for underwater sensor networks (USNs). We set an optimal next hop forwarder for each node to transmit its the sensed data. This helps to reduce distance between sender and receiver. The energy consumption is minimum. Furthermore, a node is considered an eligible forwarder node only if its next hop neighbour exists. We incorporate this mechanism to avoid void hole problem. Our technique minimizes energy consumption in the network, hence, lifespan increases. The performance of our proposed technique is validated through extensive simulations.
Copyright © 2018 Obaida Abdul Karim et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
