Open AccessQ-Learning based Routing Protocol to Enhance Network Lifetime in WSNs
Author(s) -
Arunita Kundaliya,
D. K. Lobiyal
Publication year - 2021
Publication title -
international journal of computer networks and communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.159
H-Index - 8
eISSN - 0975-2293
pISSN - 0974-9322
DOI - 10.5121/ijcnc.2021.13204
Subject(s) - computer science , reinforcement learning , routing protocol , computer network , wireless sensor network , wireless routing protocol , zone routing protocol , q learning , distributed computing , routing (electronic design automation) , artificial intelligence
In resource constraint Wireless Sensor Networks (WSNs), enhancement of network lifetime has been one of the significantly challenging issues for the researchers. Researchers have been exploiting machine learning techniques, in particular reinforcement learning, to achieve efficient solutions in the domain of WSN. The objective of this paper is to apply Q-learning, a reinforcement learning technique, to enhance the lifetime of the network, by developing distributed routing protocols. Q-learning is an attractive choice for routing due to its low computational requirements and additional memory demands. To facilitate an agent running at each node to take an optimal action, the approach considers node’s residual energy, hop length to sink and transmission power. The parameters, residual energy and hop length, are used to calculate the Q-value, which in turn is used to decide the optimal next-hop for routing. The proposed protocols’ performance is evaluated through NS3 simulations, and compared with AODV protocol in terms of network lifetime, throughput and end-to-end delay.