Dynamic Coordination of Energy and Hops in WSNs Using Reinforcement Learning Routing Algorithm

DOI

10.2991/icismme-15.2015.289How to use a DOI?

Keywords

Wireless sensor network; Routing algorithm; Reinforcement learning algorithm; Energy consumption

Abstract

In wireless sensor network, the existing reinforcement learning routing algorithm usually optimize single goal and the process of route establishment is complex. It also has problem of data forwarding control overhead. In this paper, we present a dynamic adaptive routing algorithm with feedback learning ability to balance the energy of wireless sensor network, to reduce the routing hops, and to reduce the establishment complexity. The algorithm will use the local routing information and the method of feedback to learn neighbors’ state; routing reward values will be obtained by weighted calculation according to the energy information and the hop counts information; the optimal routing strategy will be obtained by updating the Q-value of routing table.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianyong Li
AU  - Huang Wei
PY  - 2015/07
DA  - 2015/07
TI  - Dynamic Coordination of Energy and Hops in WSNs Using Reinforcement Learning Routing Algorithm
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1347
EP  - 1353
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.289
DO  - 10.2991/icismme-15.2015.289
ID  - Li2015/07
ER  -