SEASONS: Signal and Energy Aware Sensing on iNtermittent Systems
CoRR(2024)
摘要
Both energy-aware, batteryless intermittent systems and signal-aware adaptive
sampling algorithms (ASA) aim to maximize sensor data accuracy under energy
constraints in edge devices. Intuitively, combining both into a signal-
energy-aware solution would yield even better accuracy. Unfortunately, ASAs and
intermittent systems rely on conflicting energy availability assumptions. So, a
straightforward combination cannot achieve their combined benefits. Therefore,
we propose SEASONS, the first framework for signal- and energy-aware
intermittent systems. SEASONS buffers signal data in time, monitoring queue
dynamics to ensure the data is reported within a user-specified latency
constraint. SEASONS improves sensor data accuracy by 31
energy.
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