Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments

Monitoring presence of micro-plastics in the ocean and fresh waters is an important research topic due to a need to preserve marine ecosystem. Microplastics represent threats to living organisms, producing harmful effects, ultimately also having an impact on humans through the food-chain. Use of laboratory-based and in situ techniques do help in investigating density and scale of this kind of pollutants. The in-situ sensing techniques are gaining popularity due to automation and continuous availability. These techniques though need an accurate hardware and efficient computing model to achieve desired success. Here, we propose an IoT based system called ‘SmartIC’ using specialized sensors and intelligent computing tools, specifically designed for in-situ monitoring of microplastics in natural aquatic environments. This paper is focused on system architecture, monitoring process and outline of experimental work. The initial research provides very promising results. A further course of the investigation with validation will be conducted in future to establish the proposed system completely.

[1]  Amy Lusher,et al.  Microplastics in the Marine Environment: Distribution, Interactions and Effects , 2015 .

[2]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[3]  Carmen Paz Suárez Araujo,et al.  Neural Network Ensembles with Missing Data Processing and Data Fusion Capacities: Applications in Medicine and in the Environment , 2011, IWANN.

[4]  Zenon Chaczko,et al.  HyMuDS: A Hybrid Multimodal Data Acquisition System , 2015, 2015 Asia-Pacific Conference on Computer Aided System Engineering.

[5]  P.S. Hiremath,et al.  Content Based Image Retrieval Using Color, Texture and Shape Features , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[6]  Luca Mainetti,et al.  Evolution of wireless sensor networks towards the Internet of Things: A survey , 2011, SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks.

[7]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[8]  C. P. Suárez-Araujo,et al.  Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies , 2016, Environmental Science and Pollution Research.

[9]  Bashar Nuseibeh,et al.  Weaving Together Requirements and Architectures , 2001, Computer.

[10]  Richard C. Thompson,et al.  Microplastics in the marine environment: a review of the methods used for identification and quantification. , 2012, Environmental science & technology.

[11]  Richard C. Thompson,et al.  Microplastic—an emerging contaminant of potential concern? , 2007, Integrated environmental assessment and management.

[12]  Zenon Chaczko,et al.  iMuDS: An Internet of Multimodal Data Acquisition and Analysis Systems for Monitoring Urban Waterways , 2017, 2017 25th International Conference on Systems Engineering (ICSEng).

[13]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[14]  F. Regoli,et al.  Plastics and microplastics in the oceans: From emerging pollutants to emerged threat. , 2017, Marine environmental research.

[15]  Xin Yao,et al.  Designing Neural Network Ensembles by Minimizing Mutual Information , 2003 .

[16]  Zenon Chaczko,et al.  Managing Dynamism of Multimodal Detection in Machine Vision Using Selection of Phenotypes , 2013, EUROCAST.

[17]  Paul V. Zimba,et al.  Remote Sensing Techniques to Assess Water Quality , 2003 .

[18]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Carmen Paz Suárez Araujo,et al.  HUMANN-based system to identify benzimidazole fungicides using multi-synchronous fluorescence spectra: an ensemble approach. , 2009 .

[20]  J. Marques,et al.  Microplastics in Juvenile Commercial Fish from an Estuarine Environment , 2018 .

[21]  Zenon Chaczko,et al.  Evolutionary Feature Optimization and Classification for Monitoring Floating Objects , 2015, Computational Intelligence and Efficiency in Engineering Systems.