Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems
Geofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviati...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10845873/ |
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| author | Alireza Famili Shihua Sun Tolga Atalay Angelos Stavrou |
| author_facet | Alireza Famili Shihua Sun Tolga Atalay Angelos Stavrou |
| author_sort | Alireza Famili |
| collection | DOAJ |
| description | Geofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviation safety in drone management to access control within mixed reality platforms like the metaverse. Effective geofencing relies heavily on precise tracking capabilities, a critical component for maintaining the integrity and functionality of these systems. Leveraging the advantages of 5G technology, including its large bandwidth and extensive accessibility, presents a promising solution to enhance geofencing performance. In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points”, which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. Our proposed meta-RL method addresses the NP-hard problem of determining an optimal placement of 5G Points to minimize spatial geometry-induced errors. Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. We devised a comprehensive test campaign to evaluate the performance of MetaFence. Our results demonstrate that this strategic placement significantly improves tracking accuracy compared to traditional methods. Furthermore, we show that the meta-training strategy enables the learned policy to generalize effectively and perform efficiently when faced with new environments. |
| format | Article |
| id | doaj-art-d91906d1841f486286de08dd7c244dde |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-d91906d1841f486286de08dd7c244dde2025-02-06T00:00:57ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-01694496010.1109/OJCOMS.2025.353131810845873Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing SystemsAlireza Famili0https://orcid.org/0000-0002-0617-5851Shihua Sun1https://orcid.org/0009-0008-0365-1390Tolga Atalay2https://orcid.org/0000-0002-9195-4007Angelos Stavrou3https://orcid.org/0000-0001-9888-0592WayWave Inc., Arlington, VA, USADepartment of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USADepartment of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USAWayWave Inc., Arlington, VA, USAGeofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviation safety in drone management to access control within mixed reality platforms like the metaverse. Effective geofencing relies heavily on precise tracking capabilities, a critical component for maintaining the integrity and functionality of these systems. Leveraging the advantages of 5G technology, including its large bandwidth and extensive accessibility, presents a promising solution to enhance geofencing performance. In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points”, which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. Our proposed meta-RL method addresses the NP-hard problem of determining an optimal placement of 5G Points to minimize spatial geometry-induced errors. Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. We devised a comprehensive test campaign to evaluate the performance of MetaFence. Our results demonstrate that this strategic placement significantly improves tracking accuracy compared to traditional methods. Furthermore, we show that the meta-training strategy enables the learned policy to generalize effectively and perform efficiently when faced with new environments.https://ieeexplore.ieee.org/document/10845873/Geofencingtrackingmeta-RLsensor placement5G networks |
| spellingShingle | Alireza Famili Shihua Sun Tolga Atalay Angelos Stavrou Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems IEEE Open Journal of the Communications Society Geofencing tracking meta-RL sensor placement 5G networks |
| title | Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems |
| title_full | Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems |
| title_fullStr | Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems |
| title_full_unstemmed | Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems |
| title_short | Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems |
| title_sort | harnessing meta reinforcement learning for enhanced tracking in geofencing systems |
| topic | Geofencing tracking meta-RL sensor placement 5G networks |
| url | https://ieeexplore.ieee.org/document/10845873/ |
| work_keys_str_mv | AT alirezafamili harnessingmetareinforcementlearningforenhancedtrackingingeofencingsystems AT shihuasun harnessingmetareinforcementlearningforenhancedtrackingingeofencingsystems AT tolgaatalay harnessingmetareinforcementlearningforenhancedtrackingingeofencingsystems AT angelosstavrou harnessingmetareinforcementlearningforenhancedtrackingingeofencingsystems |