Precision Tracking in Geofencing Systems using Deep Reinforcement Learning

Geofencing technologies have emerged as crucial tools in establishing virtual boundaries within both physical and digital spaces, providing a secure method to manage and supervise specified zones. They are now recognized as vital instruments for delineating and managing boundaries in a range of applications, from ensuring aviation safety in drone operations to regulating access in mixed reality environments such as the metaverse. Successful geofencing depends significantly on accurate tracking, which is essential for preserving the integrity and effectiveness of these systems. Utilizing the benefits of 5G technology, such as its broad bandwidth and widespread availability, offers a promising approach to improve geofencing performance. In this paper, we present DEFENCE: Deep Reinforcement Learning for Geofencing Enhancement, an innovative method for precise geofencing that utilizes "5G Points" within indoor 5G small cell networks, optimally placed using a deep Q-learning framework. Through the computation of the Cramér-Rao Lower Bound (CRLB), we evaluate tracking errors arising from spatial configurations and ranging inaccuracies. Our proposed deep Q-learning model tackles the NP-hard challenge of identifying the optimal placement of 5G Points to reduce errors caused by spatial geometry. We implemented an extensive testing campaign to assess the efficacy of DEFENCE. Our findings reveal that this strategic deployment enhances tracking accuracy by a factor of 100 over conventional placement methods. This breakthrough considerably bolsters geofencing systems, enhancing their defense against potential threats such as unauthorized drone incursions and security breaches within metaverse environments.