IoT-based occupancy monitoring techniques for energy-efficient smart buildings

• Published in: 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) pp. 58 - 63
• Main Authors: Akkaya, Kemal, Guvenc, Ismail, Aygun, Ramazan, Pala, Nezih, Kadri, Abdullah
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2015
• Subjects: Accuracy; Big data; Buildings; Cameras; data fusion; Data integration; data mining; energy efficiency; hidden Markov model (HMM); HVAC; IEEE 802.11 Standards; localization; Markov chain; Monitoring; occupancy monitoring; position estimation; positioning; Sensors; WiFi; wireless location estimation; WLAN
• DOI: 10.1109/WCNCW.2015.7122529

With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users' smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.