Learning a Policy for Coordinated Sampling in Body Sensor Networks

This paper describes a method for learning coordination policies in body sensor networks. The learning of a compact coordination policy is important for implementing the policy in sensor nodes with limited memory. We present a novel algorithm, Reinforcement Learning Average Approximation (RLAA), to learn local coordination policies for each sensor node from globally joint rewards. These local policies are obtained by reinforcement learning and averaging state-action tables under a stochastic process model. We show results on a simulation of an existing body sensor network interfaced with transdermal sensors that demonstrate the performance of this learning scheme. Experimental results show that the performance of the RLAA algorithm is significantly better than a random policy and is close to the optimal policy that can be obtained from solving a global Markov Decision Process while the learning step is fast. The results also show that the RLAA algorithm is scalable to networks represented by large state spaces (in terms of number s of sensors and degree of discretization).