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Sensors 2016, 16(1), 53;

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Author to whom correspondence should be addressed.
Academic Editor: Davide Brunelli
Received: 18 November 2015 / Revised: 30 December 2015 / Accepted: 30 December 2015 / Published: 4 January 2016
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Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner. View Full-Text
Keywords: wireless sensor network; solar cells; energy prediction; shadow detection wireless sensor network; solar cells; energy prediction; shadow detection

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Zou, T.; Lin, S.; Feng, Q.; Chen, Y. Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors 2016, 16, 53.

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