Toward large-scale autonomous monitoring and sensing of underwater pollutants
Flores, H.; Motlagh, N.H.; Zuniga, A.; Liyanage, M.; Passananti, M.; Tarkoma, S.; Youssef, M.; Nurmi, P. (2020). Toward large-scale autonomous monitoring and sensing of underwater pollutants. arXiv (Archive) arXiv:2005.09571v1. https://dx.doi.org/10.48550/arXiv.2005.09571
In: arXiv (Archive). Cornell University. , meer
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| Trefwoord |
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| Author keywords |
AUV; Underwater drones; Marine pollution monitoring; Autonomous robots; Pervasive Sensing |
| Auteurs | | Top |
- Flores, H.
- Motlagh, N.H.
- Zuniga, A.
- Liyanage, M.
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- Passananti, M.
- Tarkoma, S.
- Youssef, M.
- Nurmi, P.
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| Abstract |
Marine pollution is a growing worldwide concern, affecting health of marine ecosystems, human health, climate change, and weather patterns. To reduce underwater pollution, it is critical to have access to accurate information about the extent of marine pollutants as otherwise appropriate countermeasures and cleaning measures cannot be chosen. Currently such information is difficult to acquire as existing monitoring solutions are highly laborious or costly, limited to specific pollutants, and have limited spatial and temporal resolution. In this article, we present a research vision of large-scale autonomous marine pollution monitoring that uses coordinated groups of autonomous underwater vehicles (AUV)s to monitor extent and characteristics of marine pollutants. We highlight key requirements and reference technologies to establish a research roadmap for realizing this vision. We also address the feasibility of our vision, carrying out controlled experiments that address classification of pollutants and collaborative underwater processing, two key research challenges for our vision. |
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