Fuzzy Power Management for Environmental Monitoring Systems in Tropical Regions

0430491 - ÚI 2015 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Watts, A.G. - Prauzek, M. - Musílek, P. - Pelikán, Emil - Sanchez-Azofeifa, A.
Fuzzy Power Management for Environmental Monitoring Systems in Tropical Regions.
Proceedings of the 2014 International Joint Conference on Neural Networks. Piscataway: IEEE Computer Society, 2014, s. 1719-1726. ISBN 978-1-4799-1484-5.
[IJCNN 2014. International Joint Conference on Neural Networks. Beijing (CN), 06.07.2014-11.07.2014]
Grant ostatní: TECTERRA(CA) 1209-UNI-013; SGS VŠB(CZ) SP2014/156
Institucionální podpora: RVO:67985807
Klíčová slova: environmental monitoring * solar energy * remote devices
Kód oboru RIV: IN - Informatika

Remote environmental monitoring systems require effective energy management to allow reliable long-term operation without frequent maintenance to replace or recharge batteries. To design and analyze relevant energy management strategies, we have developed Simulink-based models of a recently constructed monitoring device to evaluate its potential performance. The model uses long-term solar energy data from two locations, Chamela, Mexico, and Fairview, Canada, to estimate the energy harvesting capabilities of the device. Using the simulator, we have developed and evaluated a fuzzy energy management strategy that determines how the device should operate to match the solar energy profile in each location. Solar energy in Chamela, Mexico is abundant and consistent so an energy harvesting remote monitoring device could have a high activity level without risking device failure. Fairview, Canada, has limited solar resources in the winter but plenty in the summer; a device dependent upon this energy source must adapt its activity level to match energy availability or risk running out of energy. While the simulated device in Mexico outperforms the one in Canada, both succeed in matching the available environmental resources and largely avoid energy related device failure. In the future, their performance can be improved by optimizing the designed strategies and further improving the details of the simulation.
Trvalý link: http://hdl.handle.net/11104/0235409