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Energy Harvesting Sensor Nodes (EHSNs) have stringent low-energy consumption requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc.). Furthermore, no accurate models exist to predict the energy harvesting income in order to adapt at run-time the executing set of prioritized tasks. In this paper, the authors propose a novel power-aware task scheduler for EHSNs, namely, HOLLOWS: Head-Of-Line Low-Overhead Wide-priority Service. HOLLOWS uses an energy-constrained prioritized queue model to describe the residence time of tasks entering the system and dynamically selects the set of tasks to execute, according to system accuracy requirements and expected energy. Moreover, HOLLOWS includes a new energy harvesting prediction algorithm, that is, Weather-Conditioned Moving Average (WCMA), which they have developed to estimate the solar panel energy income.
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