Abstract: The proposal concerns a method of adaptive route selection in a node of a wireless mesh communication network. Such method is based on the Reinforcement Learning-based adaptive routing scheme. The proposal includes the idea of estimating initial reward values for the path discovery phase in dependence of the signal strength of the received signal and the number of hops the received route request message (RREQ) or route reply message (RREP) experienced during traversing in the network. Further improvements concern the idea of a feedback reward calculation for the routing absent the path discovery process as well as an improved method of route selection based on calculation of a temperature parameter for a Gibbs-Boltzmann distribution, which allows a dynamic adjustment of the node selection probabilities. Another improvement concerns a part of the reward function calculation, related to an exponential fallback mechanism on packet loss events.

Abstract: The proposal concerns a method of adaptive route selection in a node of a wireless mesh communication network. Such method is based on the Reinforcement Learning-based adaptive routing scheme. The proposal includes the idea of estimating initial reward values for the path discovery phase in dependence of the signal strength of the received signal and the number of hops the received route request message (RREQ) or route reply message (RREP) experienced during traversing in the network. Further improvements concern the idea of a feedback reward calculation for the routing absent the path discovery process as well as an improved method of route selection based on calculation of a temperature parameter for a Gibbs-Boltzmann distribution, which allows a dynamic adjustment of the node selection probabilities. Another improvement concerns a part of the reward function calculation, related to an exponential fallback mechanism on packet loss events.