Gaining Insight into User Behaviour and Systematically Determining User Location via Bluetooth Low Energy Beacon Optimisation

The multiuser challenge within the field of Intelligent Environments, specifically concerning Indoor Positioning systems needs to be addressed. Solving this challenge is paramount for enabling customised services in indoor locations. This investigation aims to distinguish between multiple users in an Intelligent Environment and identify their specific locations at a given time by employing a visual interface to deploy localised and personalised services to specific individuals in real-time. The investigation is conducted in the Smart Spaces Lab of Middlesex University London (i.e., a fully functional Intelligent Environment). The investigation leverages the Lab’s existing technology and uses BLE Beacons with a novel placement approach to complete the User Location challenge. User Data was also generated in the process, giving rise to many insights. On the other hand, Machine Learning was utilised to predict User Activity using the generated Data. The study also offers insight into the latest research concerning indoor positioning systems and their approaches. Additionally, the investigation benchmarks its approaches against the methods published in recent literature and reviews the limitations of this investigation, emphasising future work. Video-based evidence is provided to establish the investigation’s authenticity and complement the description in this paper.