Micro power meter for energy monitoring of wireless sensor networks at scale
Proceedings of the 6th international conference on Information processing in sensor networks
Software-based on-line energy estimation for sensor nodes
Proceedings of the 4th workshop on Embedded networked sensors
Energy Metering for Free: Augmenting Switching Regulators for Real-Time Monitoring
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Quanto: tracking energy in networked embedded systems
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Battery state-of-charge approximation for energy harvesting embedded systems
EWSN'13 Proceedings of the 10th European conference on Wireless Sensor Networks
Camazotz: multimodal activity-based GPS sampling
Proceedings of the 12th international conference on Information processing in sensor networks
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Power management of mobile embedded devices remains important with the slow growth of battery energy density relative to computing power. Estimating the state of charge (SOC) of battery is key for scheduling power intensive tasks, yet current approaches either require dedicated hardware, use battery voltage as a loose indicator of SOC, or track the net energy flow from the battery over time where inevitable small errors in instantaneous estimation can lead to large cumulative estimation errors and significantly degraded sampling strategies. In this paper, we propose a method for estimating SOC of a node's battery based on the conation of two noisy inputs: (1) the net current flow in the battery for instantaneous net energy flow estimation; and (2) the battery voltage as a measure of absolute SOC. Using empirical data from several weeks of flying fox tracking experiments, we validate our approach in terms of the accuracy of SOC prediction and show how SOC prediction can be used to adaptively schedule tasks for energy neutral operation of sensing applications.