Optimal sensing scheduling in energy harvesting sensor networks

In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from the environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the utility generated by the observations is a function of the number of the active sensing nodes in that slot. Assuming the energy harvesting processes at individual sensors are independent Bernoulli processes, our objective is to develop a sensing scheduling policy so that the expected long-term average utility generated by the sensors is maximized. Under the concavity assumption of the utility function, we first show that the expected time average utility has an upper bound for any feasible scheduling policy satisfying the energy causality constraint. We then propose a myopic policy, which aims to select a fixed number of sensors with the highest energy levels to perform the sensing task in each slot. The myopic policy essentially balances the current energy queue lengths in every time slot. We show that the time average utility generated under the myopic policy converges to the upper bound almost surely as time T approaches infinity, thus the myopic policy is optimal. The corresponding convergence rate is also explicitly characterized.