Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917549924417536 |
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| author | Lee, Joonhee Lee, Kichang Ko, Jeonggil |
| author_facet | Lee, Joonhee Lee, Kichang Ko, Jeonggil |
| contents | Wilderness monitoring tasks, such as poaching surveillance and forest fire detection, require pervasive and high-accuracy sensing. While AIoT offers a promising path, covering vast, inaccessible regions necessitates the massive deployment of maintenance-free, battery-less nodes with limited computational resources. However, these constraints create a critical `Availability Gap.' Conventional intermittent operations prioritize computation throughput, forcing sensors to sleep during energy buffering. Consequently, systems miss ephemeral, `now-or-never' events (e.g., Vocalizations of natural monuments or Fire), which is fatal for detecting rare but high-stakes anomalies. To address this, we propose an Energy-aware Elastic Split Computing Algorithm that prioritizes continuous sensing by dynamically offloading tasks to energy-rich neighbors. Preliminary results demonstrate stable monitoring of an additional $2,496\;\text{m}^2$ and the capture of approximately 103 more critical events per day. Ultimately, this algorithm establishes a robust foundation for building resilient, fail-safe surveillance systems even on resource-constrained nodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks Lee, Joonhee Lee, Kichang Ko, Jeonggil Systems and Control Wilderness monitoring tasks, such as poaching surveillance and forest fire detection, require pervasive and high-accuracy sensing. While AIoT offers a promising path, covering vast, inaccessible regions necessitates the massive deployment of maintenance-free, battery-less nodes with limited computational resources. However, these constraints create a critical `Availability Gap.' Conventional intermittent operations prioritize computation throughput, forcing sensors to sleep during energy buffering. Consequently, systems miss ephemeral, `now-or-never' events (e.g., Vocalizations of natural monuments or Fire), which is fatal for detecting rare but high-stakes anomalies. To address this, we propose an Energy-aware Elastic Split Computing Algorithm that prioritizes continuous sensing by dynamically offloading tasks to energy-rich neighbors. Preliminary results demonstrate stable monitoring of an additional $2,496\;\text{m}^2$ and the capture of approximately 103 more critical events per day. Ultimately, this algorithm establishes a robust foundation for building resilient, fail-safe surveillance systems even on resource-constrained nodes. |
| title | Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.24179 |