Journal
IEEE COMMUNICATIONS LETTERS
Volume 20, Issue 1, Pages 173-176Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2015.2497306
Keywords
Energy harvesting; energy prediction model; machine learning
Categories
Funding
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China [ICT1517]
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Energy harvesting enables perpetual operation of wireless networks without the need for battery change. In particular, energy can be harvested from radio waves in the radio frequency spectrum. To ensure a reliable performance, energy prediction modelling is a key component for optimizing energy harvesting because it equips the harvesting node with adaptation to energy availability. We use two machine learning techniques, linear regression (LR) and decision trees (DT) to model the harvested energy using real-time power measurements in the radio spectrum. Numerical results show that LR outperforms DT by attaining minimum 85% prediction accuracy. These models will be useful for defining the scheduling policies of harvesting nodes.
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