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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.18141 |
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| _version_ | 1866911607995498496 |
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| author | Ruiz-Guirola, David E. Filippou, Miltiadis Lopez, Onel A. |
| author_facet | Ruiz-Guirola, David E. Filippou, Miltiadis Lopez, Onel A. |
| contents | Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra-low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake-ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid-like IoT deployments overlook these limitations and merely rely on continuously powered sensing. Herein, we propose an energy-aware geofencing framework for camera-equipped EH IoTDs deployed around a protected area and its surrounding perimeter zone. The framework integrates a directional sensing power model with an operational representation of EH, sensing, sleeping, and reporting, accounting for the limited field-of-view (FoV) and distance-dependent detection confidence of the IoTDs. Device activity is controlled by the coverage-providing access point, which hosts a mobile edge host and a facility geocencing system to ensure timely and reliable detection under tight energy constraints. Reinforcement learning is used to determine IoTD placement, enabling earlier intruder detection than uniform grid-based deployments. Numerical results show that the proposed coordinated sensing and reporting configuration achieves frugal geofencing with fewer devices, while concurrently improving detection timeliness and dependability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18141 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Frugal Geofencing via Energy-aware Sensing and Reporting Ruiz-Guirola, David E. Filippou, Miltiadis Lopez, Onel A. Systems and Control Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra-low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake-ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid-like IoT deployments overlook these limitations and merely rely on continuously powered sensing. Herein, we propose an energy-aware geofencing framework for camera-equipped EH IoTDs deployed around a protected area and its surrounding perimeter zone. The framework integrates a directional sensing power model with an operational representation of EH, sensing, sleeping, and reporting, accounting for the limited field-of-view (FoV) and distance-dependent detection confidence of the IoTDs. Device activity is controlled by the coverage-providing access point, which hosts a mobile edge host and a facility geocencing system to ensure timely and reliable detection under tight energy constraints. Reinforcement learning is used to determine IoTD placement, enabling earlier intruder detection than uniform grid-based deployments. Numerical results show that the proposed coordinated sensing and reporting configuration achieves frugal geofencing with fewer devices, while concurrently improving detection timeliness and dependability. |
| title | Frugal Geofencing via Energy-aware Sensing and Reporting |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.18141 |