Terracorder: Sense Long and Prosper
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912118143451136 |
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| author | Millar, Josh Sethi, Sarab Haddadi, Hamed Madhavapeddy, Anil |
| author_facet | Millar, Josh Sethi, Sarab Haddadi, Hamed Madhavapeddy, Anil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Terracorder: Sense Long and Prosper Millar, Josh Sethi, Sarab Haddadi, Hamed Madhavapeddy, Anil Machine Learning 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. |
| title | Terracorder: Sense Long and Prosper |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.02407 |