Online Energy Harvesting Prediction in Environmentally-Powered Wireless Sensor Networks
|Research Area:||Design, evaluation and test of complete solutions for Green Wireless Sensor Networks||Year:||2016|
|Type of Publication:||Article|
|Authors:||Alessandro Cammarano; Chiara Petrioli; Dora Spenza|
|Journal:||IEEE Sensors Journal|
The increasing popularity of micro-scale power-scavenging techniques for Wireless Sensor Networks (WSNs) is paving the way to energy-autonomous sensing systems. To sustain perpetual operations, however, environmentally-powered devices must adapt their workload to the stochastic nature of ambient sources. Energy prediction models, which estimate the future expected energy intake, are effective tools to support the development of proactive power management strategies. In this work, we present Pro-Energy, an energy prediction model for multi-source Energy-Harvesting WSNs that leverages past energy observations to forecast future energy availability. We then propose Pro-Energy-VLT, an extension of Pro-Energy that combines our energy predictor with timeslots of variable lengths to adapt to the dynamics of the power source. To assess the performance of our proposed solutions, we use real-life solar and wind traces, as well as publicly-available traces of solar irradiance and wind speed. A comparative performance evaluation shows that Pro-Energy significantly outperforms state-of-the-art energy predictors, by improving the prediction accuracy of up to 67%. Moreover, by adapting the granularity of the prediction timeslots to the dynamics of the energy source, Pro-Energy-VLT further improves the prediction accuracy, while reducing the memory footprint and the energy overhead of energy forecasting.
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