تقرير
Terracorder: Sense Long and Prosper
| العنوان: | Terracorder: Sense Long and Prosper |
|---|---|
| المؤلفون: | Millar, Josh, Sethi, Sarab, Haddadi, Hamed, Madhavapeddy, Anil |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage. We introduce Terracorder -- a versatile multi-sensor device -- and showcase its exceptionally low power consumption using an on-device reinforcement learning scheduler. We prototype a unique device setup for biodiversity monitoring and compare its battery life using our scheduler against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. We then explore how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness. Comment: Preprint |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.02407 |
| رقم الأكسشن: | edsarx.2408.02407 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |