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Internet of Things forms the backbone of modern building applications.Wireless sensors are being increasingly adopted for their flexibility andreduced cost of deployment. However, most wireless sensors are powered bybatteries today and large deployments are inhibited by manual batteryreplacement. Energy harvesting sensors provide an attractive alternative, butthey need to provide adequate quality of service to applications givenuncertain energy availability. We propose using reinforcement learning tooptimize the operation of energy harvesting sensors to maximize sensing qualitywith available energy. We present our system ACES that uses reinforcementlearning for periodic and event-driven sensing indoors with ambient lightenergy harvesting. Our custom-built board uses a supercapacitor to store energytemporarily, senses light, motion events and relays them using Bluetooth LowEnergy. Using simulations and real deployments, we show that our sensor nodesadapt to their lighting conditions and continuously sends measurements andevents across nights and weekends. We use deployment data to continually adaptsensing to changing environmental patterns and transfer learning to reduce thetraining time in real deployments. In our 60 node deployment lasting two weeks,we observe a dead time of 0.1%. The periodic sensors that measure luminosityhave a mean sampling period of 90 seconds and the event sensors that detectmotion with PIR captured 86% of the events on average compared to abattery-powered node.

ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning