Several wireless sensor network (WSN) applications leverage energy harvesting technologies such as small size photo-voltaic modules. The advantage of solar energy over other forms of environmental energy is that the available solar power can be predicted with reasonable accuracy allowing the implementation of efficient power management techniques. However accurate predictions of future energy profiles can be expensive in term of memory occupancy and complexity and a trade-off between accuracy and computational effort must be evaluated. In this paper we compare different solar energy prediction algorithms that give estimates future available energy over the time. They are computationally simple and have a small memory footprint to facilitate the implementation in resource limited solar harvesting sensor nodes. Simulation results show that the most effective predictors is possible achieve high accuracy, diverging from real energy profile by less than 10%.

Algorithms for Harvested Energy Prediction in Batteryless Wireless Sensor Networks

Brunelli, Davide;
2009-01-01

Abstract

Several wireless sensor network (WSN) applications leverage energy harvesting technologies such as small size photo-voltaic modules. The advantage of solar energy over other forms of environmental energy is that the available solar power can be predicted with reasonable accuracy allowing the implementation of efficient power management techniques. However accurate predictions of future energy profiles can be expensive in term of memory occupancy and complexity and a trade-off between accuracy and computational effort must be evaluated. In this paper we compare different solar energy prediction algorithms that give estimates future available energy over the time. They are computationally simple and have a small memory footprint to facilitate the implementation in resource limited solar harvesting sensor nodes. Simulation results show that the most effective predictors is possible achieve high accuracy, diverging from real energy profile by less than 10%.
2009
3rd IEEE International Workshop on Advances in Sensors and Interfaces
Washington, DC, USA
IEEE
9781424447084
C., Bergonzini; Brunelli, Davide; L., Benini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/82692
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