An adaptive energy-efficient MAC protocol for wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
An adaptive coordinated medium access control for wireless sensor networks
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Node density control for maximizing wireless sensor network lifetime
International Journal of Network Management
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decentralized Learning in Markov Games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Theoretical considerations of potential-based reward shaping for multi-agent systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Autonomous Agents and Multi-Agent Systems
Wireless Personal Communications: An International Journal
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In this work we present a reinforcement learning algorithm that aims to increase the autonomous lifetime of a Wireless Sensor Network (WSN) and decrease its latency in a decentralized manner. WSNs are collections of sensor nodes that gather environmental data, where the main challenges are the limited power supply of nodes and the need for decentralized control. To overcome these challenges, we make each sensor node adopt an algorithm to optimize the efficiency of a small group of surrounding nodes, so that in the end the performance of the whole system is improved. We compare our approach to conventional ad-hoc networks of different sizes and show that nodes in WSNs are able to develop an energy saving behaviour on their own and significantly reduce network latency, when using our reinforcement learning algorithm.