Rapid Internet of Things (IoT) prototype for accurate people counting towards energy efficient buildings

Abstract

According to the U.S. Department of Energy, a significant portion of energy used in buildings is wasted. If the occupancy quantity in a pre-determined thermal zone is aware, a building automation system (BAS) is able to intelligently adjust the building operation to provide “just-enough” heating, cooling, and ventilation capacities to building users. Therefore, an occupancy counting device that can be widely deployed at low prices with low failure rate, small form-factor, good usability, and conserved user privacy is highly desirable. Existing occupancy detection or recognition sensors (e.g., passive infrared, camera, acoustic, RFID, CO2) cannot meet all these above system requirements. In this work, we present an IoT (Internet of Things) prototype that collects room occupancy information to assist in the operation of energy-efficient buildings. The proposed IoT prototype consists of Lattice iCE40-HX1K stick FPGA boards and Raspberry Pi modules. Two pairs of our prototypes are installed at a door frame. When a person walks through this door frame, blocking of active infrared streams between both pairs of IoT prototypes is detected. The direction of human movement is obtained through comparing occurrence time instances of two obstructive events. Thus, the change in occupancy quantity of a thermal zone is calculated and updated. Besides, an open-source application user interface is developed to allow anonymous users or building automation systems to easily acquire room occupancy information. We carry out a three-month random test of human entry and exit of a thermal zone, and find that the occupancy counting accuracy is 97%. The proposed design is completely made of off-the-shelf electronic components and the estimated cost is less than $160. To investigate the impact on building energy savings, we conduct a building energy simulation using EnergyPlus and find the payback period is approximately 4 months. In summary, the proposed design is miniature, non-intrusive, ease of use, low failure rate, and cost-effective for smart buildings.

Employing entropy measures to identify visitors in multi-occupancy environments

“…Several other techniques are utilised to identify and detect activities in multi-occupancy environments. Recently, some research works have focused on indoor detection, either in algorithms or system developments, of a single person in a multi-occupancy environment (Nguyen et al 2020;Shen et al 2017;Zhao et al 2018;Huang et al 2016Huang et al , 2019. Benmansour et al (2017) investigated the challenge of modelling multi-occupancy activities.…”

Understanding Occupant Behaviors in Dynamic Environments using OBiDE framework

“…While the previous section provides a brief overview of different types of sensors available for capturing different occupant behaviors, but occupants' movements and presence are the pre-requisites for any type of occupant behaviors' understanding. Existing studies on occupancy detection and proximity analysis have been dominated by a wide range of sensors including passive infrared, Radio-Frequency Identification (RFID), ultrasonic, acoustic recognition, image cameras, Wi-Fi-based, Global Positioning System (GPS) and Bluetooth Low Energy (BLE) technology [38]. Each technology has its own advantages and disadvantages which can be found in Huang et al [38] research.…”

Development of a Sensor to Measure Physician Consultation Times

“…Therefore a delay between two lines (vertical planes) has to be measured. Huang et al [11] showed a successful installation of a bi-directional system, which provides additional inputs for the building automation system. Therefore, infrared sources are mounted on one side of the door and the detector is mounted on the other side.…”