Sin Man (Abi) CHoi

Research

Subject

Understanding Space Utilisation and Privacy-enhanced Occupancy Monitoring via the Application of Internet of Things Deployments: Embedding and Communicating real-time data with Edge AI

First and second supervisors

Abstract

s the world moves towards Industry 4.0, there is an increased demand for digitalising the environment and monitoring various spaces. One heavily focused metric is occupancy data. This data can be used to optimise operations in buildings in various ways, such as Heating, Ventilation, and Air Conditioning (HVAC) optimisation, light control, space planning, building maintenance and even occupants’ health. Installing sensors around spaces allows us to collect occupancy data at different occupancy resolution, where occupancy resolution can be categorised into 3 dimensions, occupant resolution, which refers to the presence, count, location, identity and activity of the occupants; spatial resolution, defined as scale of the monitored space, ranging from zones and rooms to entire floors and buildings; and temporal resolution, defined as the smallest time changed in spatial and occupant resolution (Melfi et al., 2011).

Despite the advantages occupancy data can bring, the use of occupancy data raises significant privacy concerns, as it involves monitoring individuals within a space. While occupancy data is important for improving operational efficiency, its collection, management and storage must be done in ways that respect occupants’ privacy.

This research project aims to develop a privacy-preserving, non-intrusive, end-to-end occupancy monitoring system. The system will employ Internet of Things (IoT) sensors, deep learning algorithms and edge computing to capture and process occupancy data at various resolutions to measure occupancy data in a zone with accuracy at minutes, gain insights into occupancy patterns, and provide users with visualisation of the occupancy data to aid their understanding towards space utilisation.

Biography

Abi studied a BEng in Electrical and Electronic Engineering at the University of Bath and an MSc in Connected Environment at UCL. She started her PhD at the Connected Environment Lab in 2023-24, supervised by Prof. Andy Hudson-Smith and Dr Duncan Wilson. Her research focuses on utilising the Internet of Things, sensors, artificial intelligence, data analysis and data visualisation to sense, communicate and understand the occupancy data in a building.