Design considerations for solar energy harvesting wireless embedded systems
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Design of a solar-harvesting circuit for batteryless embedded systems
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Adaptive Power Management for Environmentally Powered Systems
IEEE Transactions on Computers
Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters
Microelectronics Journal
Evaluation and design exploration of solar harvested-energy prediction algorithm
Proceedings of the Conference on Design, Automation and Test in Europe
SunCast: fine-grained prediction of natural sunlight levels for improved daylight harvesting
Proceedings of the 11th international conference on Information Processing in Sensor Networks
MASS '12 Proceedings of the 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)
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Solar harvest prediction is used in energy-harvesting sensor networks to achieve perpetual node operation. Existing approaches only exploit local knowledge and thus fail in unforeseeable, changing weather conditions. We investigate the benefit of incorporating global knowledge in terms of fractional sky cloudiness, so-called cloud cover. We propose and evaluate two methods that combine local information of a node's harvest pattern with global cloud cover forecasts. We evaluate their performance with solar traces collected by three solar-harvesting sensor nodes and compare the results with existing prediction algorithms. We find that (i) harvest predictions using cloud cover forecasts improve overall prediction precision, (ii) prediction errors in changing weather conditions are considerably reduced, and (iii) coarse-grained cloud cover forecasts require low extra network traffic while sacrificing little prediction precision.