PENGUIN
| Autor: | Sasu Tarkoma, Agustin Zuniga, Petteri Nurmi, Huber Flores, Moustafa Youssef, Monica Passananti, Naser Hossein Motlagh, Mohan Liyanage |
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| Přispěvatelé: | Department of Computer Science, Content-Centric Structures and Networking research group / Sasu Tarkoma, Helsinki Institute for Information Technology |
| Rok vydání: | 2020 |
| Předmět: |
UAV
02 engineering and technology 010501 environmental sciences plastic classification plastic detection 01 natural sciences autonomous underwater vehicles AUV marine pollution multi-drone pervasive sensing plastic pollution unmanned vehicles Marine pollution 0202 electrical engineering electronic engineering information engineering Marine ecosystem 14. Life underwater Underwater 1172 Environmental sciences 0105 earth and related environmental sciences Remote sensing Pollutant Aerial imaging Aquatic ecosystem 020206 networking & telecommunications 113 Computer and information sciences 13. Climate action Temporal resolution Environmental science Plastic pollution |
| Zdroj: | DroNet@MobiSys |
| DOI: | 10.1145/3396864.3399704 |
| Popis: | Underwater plastic pollution is a significant global concern, affecting everything from marine ecosystems to climate change and even human health. Currently, obtaining accurate information about aquatic plastic pollutants at high spatial and temporal resolution is difficult as existing methods are laborious (e.g., dive surveys), restricted to a subset of plastics (e.g., aerial imaging for floating debris), have limited resolution (e.g., beach surveys), or are unsuited for aquatic environments (e.g., wireless sensing or Fourier-transform infrared spectroscopy). We propose PENGUIN, a work-in-progress AUV-based solution for identifying and classifying aquatic plastic pollutants. PENGUIN has been designed as the first system that can both recognize pollutants and classify them according to specifics of the material. We present the overall design of PENGUIN, introducing the different components of the architecture, and presenting current status of development. We also present results of plastic classification experiments using optical sensing, demonstrating that simple PPG sensors provide a low-cost and energy-efficient solution for classifying different plastics. Our solution can easily monitor larger underwater areas than what current techniques offer while at the same time capturing a wider range of pollutants. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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