Detecting Theft over Pattern Change Recognition and Locating Stolen Micro Mobility Vehicles in Uncovered Signals Areas

Property theft, including vehicle theft, has become a significant concern globally. Technological advancements are providing smart solutions to address issues like vehicle theft. This research aims to enhance indoor localization by developing a solution for asset tracking. The emergence of micro-mobility vehicles, such as e-scooters and e-bikes, presents an opportunity to promote sustainable urban transportation. However, micro-mobility businesses—direct-to-consumer companies and sharing platforms—face significant challenges in fleet management and theft prevention. The present study proposes a “Bluetooth Low Energy (BLE) beacon” solution to help these businesses protect their fleets, even in worst-case scenarios. The research objectives are twofold: to track stolen micro-mobility vehicles and to trace lost assets accurately, using a long transmission range of up to 500 meters. The proposed approach enables operators to accurately locate stolen scooters through indoor mapping, leveraging the BLE beacon's extended transmission range and a battery life of up to 20 years. Research designs consist of artifacts such as constructs, models, methods, and instantiations created to achieve the goals of scientific inquiry into the design. The artifact represents a comprehensive framework for improving asset tracing in challenging environments.