Technical Note: \cal Q-Learning
Machine Learning
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Reinforcement Learning
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Adaptive Routing for Sensor Networks using Reinforcement Learning
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
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The use of wireless sensor networks in industry has been increased past few years, bringing multiple benefits compared to wired systems, like network flexibility and manageability. Such networks consist of a possibly large number of small and autonomous sensor and actuator devices with wireless communication capabilities. The data collected by sensors are sent -- directly or through intermediary nodes along the network -- to a base station called sink node. The data routing in this environment is an essential matter since it is strictly bounded to the energy efficiency, thus the network lifetime. This work investigates the application of a routing technique based on reinforcement learning's Q-learning algorithm to a wireless sensor network by using an NS-2 simulated environment. Several metrics like routing overhead, data packet delivery rates and delays are used to validate the proposal comparing it with another solutions existing in the literature.