Adaptive Online Sampling of Periodic Processes with Application to Coral Reef Acoustic Abundance Monitoring. McCammon, S., Aoki, N., Mooney, T., A., & Girdhar, Y. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11671-11678, 10, 2022. IEEE. ![Adaptive Online Sampling of Periodic Processes with Application to Coral Reef Acoustic Abundance Monitoring [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download In this paper, we present an approach that enables long-term monitoring of biological activity on coral reefs by extending mission time and adaptively focusing sensing resources on high-value periods. Coral reefs are one of the most biodiverse ecosystems on the planet; yet they are also among the most imperiled: facing bleaching, ecological community collapses due to global climate change, and degradation from human activities. Our proposed method improves the ability of scientists to monitor biological activity and abundance using passive acoustic sensors. We accomplish this by extracting periodicities from the observed abundance, and using them to predict future abundance. This predictive model is then used with a Monte Carlo Tree Search planning algorithm to schedule sampling at periods of high biological activity, and power down the sensor during periods of low activity. In simulated experiments using long-term acoustic datasets collected in the US Virgin Islands, our adaptive Online Sensor Scheduling algorithm is able to double the lifetime of a sensor while simultaneously increasing the average observed acoustic activity by 21%.
@inproceedings{
title = {Adaptive Online Sampling of Periodic Processes with Application to Coral Reef Acoustic Abundance Monitoring},
type = {inproceedings},
year = {2022},
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abstract = {In this paper, we present an approach that enables long-term monitoring of biological activity on coral reefs by extending mission time and adaptively focusing sensing resources on high-value periods. Coral reefs are one of the most biodiverse ecosystems on the planet; yet they are also among the most imperiled: facing bleaching, ecological community collapses due to global climate change, and degradation from human activities. Our proposed method improves the ability of scientists to monitor biological activity and abundance using passive acoustic sensors. We accomplish this by extracting periodicities from the observed abundance, and using them to predict future abundance. This predictive model is then used with a Monte Carlo Tree Search planning algorithm to schedule sampling at periods of high biological activity, and power down the sensor during periods of low activity. In simulated experiments using long-term acoustic datasets collected in the US Virgin Islands, our adaptive Online Sensor Scheduling algorithm is able to double the lifetime of a sensor while simultaneously increasing the average observed acoustic activity by 21%.},
bibtype = {inproceedings},
author = {McCammon, Seth and Aoki, Nadege and Mooney, T. Aran and Girdhar, Yogesh},
doi = {10.1109/IROS47612.2022.9982217},
booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}
}
Downloads: 1
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