Optimal Management of Rechargeable Biosensors in Temperature-Sensitive Environments

Abstract

Biological sensors (biosensors, for short) are tiny wireless devices attached or implanted into the body of a human or animal to monitor and detect abnormalities and then relay data to physician or provide therapy on the spot. They are distinguished from conventional sensors by their biologically derived sensing elements and by being temperature constrained. Biosensors generate heat when they transmit their measurements and when they are recharged by electromagnetic energy. The generated heat translates to a temperature increase in the tissues surrounding the biosensors. If the temperature increase exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem of finding an optimal policy for operating a rechargeable biosensor inside a temperature-sensitive environment characterized by a strict maximum temperature increase constraint. This problem can be formulated as a Markov Decision Process (MDP) and solved to obtain the optimal policy which maximizes the average number of samples that can be generated by the biosensor while observing the constraint on the maximum safe temperature level. In order to handle large-size MDP models, it is shown how operating policies can be obtained using Q-learning and heuristics. Numerical and simulation results demonstrating the performance of the different policies are presented.