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Data-driven stochastic scheduling for solar-powered sensor communications

This paper presents a data-driven approach of finding optimal scheduling policies for a solar-powered sensor node that attempts to maximize net bit rates by adapting its transmission to the changes of channel fading and battery recharge. The problem is formulated as a discounted Markov decision process (MDP) framework, whereby the energy harvesting process is stochastically quantized into several representative solar states with distinct energy arrivals and is totally driven by historical data records at a sensor node. We evaluate the average net bit rate of the optimal transmission scheduling policy, and computer simulations show that the proposed policy significantly outperforms other schemes with or without the knowledge of short-term energy harvesting and channel fading patterns.