Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance wi...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2017-01, Vol.2017 (2017), p.1-14
Main Authors: Pitsillides, Andreas, Saleem, Muhammad, Saleem, Umber, Qureshi, Hassaan Khaliq, Muhammad, Petros, Lestas, Marios
Format: Article
Language:English
Subjects:
Accuracy
Complexity
Energy consumption
Energy management
Internet of Things
Irradiance
Machine learning
Model accuracy
Neural networks
Performance evaluation
Performance measurement
Power consumption
Prediction models
Sensors
Stochastic models
Weather
Wind power
Wireless networks
Wireless sensor networks
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Summary:We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.
ISSN:1530-8669
1530-8677
DOI:10.1155/2017/6928325