Exploiting Reinforcement Learning for Multiple Sink Routing in WSNs

Efficiently moving sensor data from its collection to use points is both the fundamental and the most difficult challenge in wireless sensor networks, as any data movement incurs cost. In this work, we focus on routing data to multiple, possibly mobile sinks. To deal with the dynamics of the environment arising from mobility and failures, we choose a reinforcement learning approach where neighboring nodes exchange small amounts of information allowing them to learn the next, best hop to reach all sinks. Preliminary evaluation demonstrates that our technique results in low cost routes with low overhead for the learning process.