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A key problem in sensor networks equipped with renewable energy sources is deciding how to allocate energy to various tasks (sensing, communication etc.) over time so that the deployed network continues to gather high-quality data. The state-of-the-art energy allocation algorithm takes into account current battery level and harvesting energy and fairly allocates as much energy as possible along the time dimension. In this paper we show that by not considering application-context this approach leads to very high and uniform sampling rates. However, sampling the environment at fixed predefined intervals is neither possible (need to accommodate system failures) nor desirable (sampling rate might not capture an important event with desired fidelity). To that end, in this paper we propose a novel interactive power management technique that adapts sampling rate as a function of both application-level context (e.g., user request) and system-level context (e.g harvesting energy availability). We vary several key parameters including application request patterns, geographic locations, time slot length, battery end point voltage and evaluate the performance of our approach in terms of energy efficiency and accuracy. Our simulations use sensor data and system specifications (battery and solar panel specs, sensing and communication costs) from a real sensor network deployment. Our results show that the proposed approach saves significant amounts of energy by avoiding oversampling when application does not need it while using this saved energy to support sampling at high rates to capture events with necessary fidelity when needed. The computational complexity of our approach is lower (O(n)) than the state-of-the-art noninteractive energy allocation algorithm (O(n 2)).

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