Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks

JoonHee Lee, Kichang Lee, Jeonggil Ko

Keywords: Wilderness Monitoring, Battery-less AIoT, Intermittent Computing, Split Computing, Continuous Surveillance, Decentralized Coordination, Ephemeral Events, Collaborative Inference, Deep Neural Networks (DNN)

Abstract:

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.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 11