Networked and Embedded Systems Laboratory

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Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm which allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives:  (1) Achieving energy neutral operation, i.e. energy consumption should not be more than the energy provided by the environment, (2) maximizing the system performance based on an application utility model subject to the above constraint, and (3) being adaptive to dynamics of energy source in real-time.  For practical application, we discuss a prediction model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive a theoretically optimal bound on the maximum performance achievable assuming perfect knowledge about the future. Our methods are then evaluated on a prototype solar energy harvesting platform, Heliomote, and we show that our algorithm can utilize up to 58% of the energy which would be wasted if no harvesting aware power management is used

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