RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning

Abstract

Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.

Keywords

• ambience monitoring
• antenna array
• deep learning
• internet of things (iot)
• rectenna
• rf energy harvesting
• time series prediction