Predictive Power Management for Wind Powered Wireless Sensor Node

Yin Wu, Bowen Li and Fuquan Zhang
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Yin Wu: College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Bowen Li: College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Fuquan Zhang: College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

Future Internet, 2018, vol. 10, issue 9, 1-21

Abstract: A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.

Keywords: energy harvesting; wireless sensor node; power management; maximal power transferring tracking; wind energy prediction; transmission power control (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
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