지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.4
- 수록면
- 107 - 114 (8page)
- DOI
- 10.14372/IEMEK.2024.19.2.107
이용수
초록· 키워드
오류제보하기
Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without
relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in
selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of
nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such
networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing
scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts
itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes
Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and
the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive
adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning
process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for
better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better
performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network
environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement
learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad
hoc networking.
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