Wireless sensor networks (WSNs) could potentially help in the measurement and monitoring of noise levels, an important step in mitigating and fighting noise pollution. Unfortunately, the high energy required by the noise measurement process and the reliance of sensor motes on batteries make the management of noise-sensing WSNs cumbersome. Giving motes energy harvesting (EH) capabilities could alleviate such a problem, and several EH-WSNs have already been demonstrated. Nevertheless, the high-frequency nature of the data required to measure noise places significant additional challenges to the design of EH-WSNs. In this paper, we present a design and prototype for a mote extension which enables the mote to detect noise levels while being powered by energy harvesting. The noise level detection carried out by the system relies primarily on the concept of peak detection. Results of performance testing are presented. Aside from the hardware design and prototype, we also discuss methods of assigning charge times for application scenarios where there are multiple pulse loads. We also propose a new opportunistic method for charge time determination. Experiments demonstrate that the new method could improve analytically-derived duty cycles by at least 350%.
|Journal||Eurasip Journal on Wireless Communications and Networking|
|Publication status||Published - 1 Dec 2014|
Bibliographical noteFunding Information:
This work is funded in part by the UK Technology Strategy Board (TSB) Emerging Technologies Programme, Project 131187/26835-183208, OPV-based Energy Harvesting for Urban Noise Pollution. Author W. M. Tan is supported by the Republic of the Philippines’ Engineering Research and Development for Technology (ERDT) Program. We are grateful to colleagues at New York’s Center for Urban Science and Progress (CUSP) for their input into this research. We are also grateful to AVNET Abacus for the gift of the Cymbet EVAL-09 evaluation boards.
© 2014, Tan and Jarvis; licensee Springer.
- Energy harvesting
- Wireless sensor networks
ASJC Scopus subject areas
- Signal Processing
- Computer Science Applications
- Computer Networks and Communications