@article{M6C004D26,
title = "Equal Energy Consumption Routing Protocol Algorithm Based on
Q-Learning for Extending the Lifespan of Ad-Hoc Sensor Network",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2021",
issn = "2287-5891",
doi = "https://doi.org/10.3745/KTCCS.2021.10.10.269",
author = "Kim Ki Sang/Kim Sung Wook",
keywords = "Reinforcement Learning, Q-Learning, Ad-Hoc Sensornetwork, Energy Consumption",
abstract = "Recently, smart sensors are used in various environments, and the implementation of ad-hoc sensor networks (ASNs) is a hot research
topic. Unfortunately, traditional sensor network routing algorithms focus on specific control issues, and they can’t be directly applied to
the ASN operation. In this paper, we propose a new routing protocol by using the Q-learning technology, Main challenge of proposed
approach is to extend the life of ASNs through efficient energy allocation while obtaining the balanced system performance. The proposed
method enhances the Q-learning effect by considering various environmental factors. When a transmission fails, node penalty is accumulated
to increase the successful communication probability. Especially, each node stores the Q value of the adjacent node in its own Q table.
Every time a data transfer is executed, the Q values are updated and accumulated to learn to select the optimal routing route. Simulation
results confirm that the proposed method can choose an energy-efficient routing path, and gets an excellent network performance compared
with the existing ASN routing protocols."
}