Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks
Simulation Modelling Practice and Theory
Nowadays, mobile applications need the location of the running devices to operate properly. This has increased the interest in indoor localization. Furthermore, the ability to sense mobile devices in indoor environments opens the door for building occupancy-count estimation. Studies have shown that occupant's detection and building occupancy-count estimation can be utilized to improve the efficiency of building operation and management. This research introduces new models to study the performance of such indoor localization and building occupancy-count estimation using the available technological advances in 5G Ultra-Dense Networks (UDNs). We propose an algorithm to collect the Received Signal Strength Indicator (RSSI) from User Equipments (UEs) and use it to build a fingerprinting database. We then use Machine Learning (ML) to estimate the location of the UEs in buildings from their RSSI values. Detecting users in the building is treated as a binary-classification problem. We then use various ML algorithms to build models for indoor occupancy-count estimation. Finally, the localization of users is used to estimate occupancy in specific sections of the building. The simulation results show that UDNs can provide accurate indoor localization, occupancy-count estimation in a building and in parts within the building.
5G, Deep learning, Fingerprinting, Machine learning, Neural networks, UDNs
A. Al-Habashna, G. Wainer and M. Aloqaily, "Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks", Simulation Modelling Practice and Theory, vol. 118, July 2022, doi: 10.1016/j.simpat.2022.102543