Optimal strategies for communication and remote estimation with an energy harvesting sensor

We consider a remote estimation problem with an energy harvesting sensor and a remote estimator. The sensor observes the state of a discrete-time source which may be a finite state Markov chain or a multidimensional linear Gaussian system. It harvests energy from its environment (say, for example, through a solar cell) and uses this energy for the purpose of communicating with the estimator. Due to randomness of the energy available for communication, the sensor may not be able to communicate all of the time. The sensor may also want to save its energy for future communications. The estimator relies on messages communicated by the sensor to produce real-time estimates of the source state. We consider the problem of finding a communication scheduling strategy for the sensor and an estimation strategy for the estimator that jointly minimizes the expected sum of communication and distortion costs over a finite time horizon. Our goal of joint optimization leads to a decentralized decision-making problem. By viewing the problem from the estimator's perspective, we obtain a dynamic programming characterization for the decentralized decision-making problem that involves optimization over functions. Under some symmetry assumptions on the source statistics and the distortion metric, we show that an optimal communication strategy is described by easily computable thresholds and that the optimal estimate is a simple function of the most recently received sensor observation.