Powering nodes of wireless sensor networks with energy harvesters for intelligent buildings: A review

Intelligent buildings play a fundamental role in achieving efficient energy management in the building sector in many countries worldwide. Improving energy consumption within a building can represent significant financial savings and reduce carbon emissions. However, intelligent buildings may impose additional burdens and challenges in their energy use. Wireless sensor networks are essential for the control systems of most intelligent building systems. There are still opportunities to reduce the power supplied to their sensor nodes in these networks. These nodes require power levels that range from microwatts to milliwatts. Typically, primary non-rechargeable batteries provide power to the nodes. Replacing or replenishing these batteries could become an impractical activity with detrimental environmental impacts. Energy harvesters (EHs) appear as a potential solution to this problem, as they could complement the use of batteries while extending their lifetimes at the sensor nodes. This paper provides a comprehensive review of the energy harvesting technologies currently under an experimental or development phase. Thus, one of the objectives here is to study if EHs can power by themselves or support current battery-powered sensor nodes at the building level. The environmental sources considered for harvester power extraction were mechanical motion, thermal, light, radio frequency, and fluid flow. Also, the potential sites and building systems for extracting power through these harvesters are presented and reported.& nbsp;This paper includes the review of the challenges and opportunities for EHs depending on the mounting location and the particular characteristics of each EH technology. The research findings in this paper demonstrate that harvesters at the buildings can generate enough power to partially supply the power requirements for sensor nodes and even completely cover the power demands of specific operation modes. (C)& nbsp;2022 The Author(s). Published by Elsevier Ltd.& nbsp;