Toward Large-Scale Autonomous Marine Pollution Monitoring

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http://hdl.handle.net/10138/329148

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Flores , H , Hossein Motlagh , N , Zuniga Corrales , A , Liyanage , M , Passananti , M , Tarkoma , S , Youssef , M & Nurmi , P 2021 , ' Toward Large-Scale Autonomous Marine Pollution Monitoring ' , IEEE Internet of Things Magazine , vol. 4 , no. 1 , pp. 40-45 . https://doi.org/10.1109/IOTM.0011.2000057

Title: Toward Large-Scale Autonomous Marine Pollution Monitoring
Author: Flores, Huber; Hossein Motlagh, Naser; Zuniga Corrales, Agustin; Liyanage, Mohan; Passananti, Monica; Tarkoma, Sasu; Youssef, Moustafa; Nurmi, Petteri
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Date: 2021-03
Number of pages: 6
Belongs to series: IEEE Internet of Things Magazine
ISSN: 2576-3199
URI: http://hdl.handle.net/10138/329148
Abstract: Marine pollution is a growing worldwide concern, affecting the health of marine ecosystems, human health, 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 the 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.
Subject: 113 Computer and information sciences
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