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Golden Seal Project: An IoT-Driven Framework for Marine Litter Monitoring and Public Engagement in Tourist Areas
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Researchers developed the Golden Seal project, an IoT-driven framework that deploys sensor networks and gamified recycling systems in coastal tourist areas to monitor and reduce marine litter while increasing public engagement with plastic pollution issues.
This paper presents the research outcomes of the Golden Seal project, which addresses the omnipresent issue of plastic pollution in coastal areas while enhancing their touristic value through the deployment of Internet of Things (IoT) technologies integrated into a gamified recycling framework. The developed system employs an IoT-enabled Wireless Sensor Network (WSN) to systematically collect, transmit, and analyze environmental data. A centralized, cloud-based platform supports real-time monitoring and data integration from Unmanned Aerial and Surface Vehicles (UAV and USV) equipped with sensors and high-resolution cameras. The system also introduces the Beach Cleanliness Index (BCI), a composite indicator that integrates quantitative environmental metrics with user-generated feedback to assess coastal cleanliness in real time. A key innovation of the project’s architecture is the incorporation of a Serious Game (SG), designed to foster public awareness and encourage active participation by local communities and municipal authorities in sustainable waste management practices. Pilot implementations were conducted at selected sites characterized by high tourism activity and accessibility. The results demonstrated the system’s effectiveness in detecting and classifying plastic waste in both coastal and terrestrial settings, while also validating the potential of the Golden Seal initiative to promote sustainable tourism and support marine ecosystem protection.
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IoT Driven Smart Innovation for Microplastic Monitoring and Detection: A Multidimensional Review
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This review evaluates IoT-integrated systems for real-time microplastic monitoring and detection in water, covering sensor technologies, predictive modeling, and risk assessment frameworks applied to common polymer types including PP, PE, PVC, and PET. Scalable IoT-based detection infrastructure could transform environmental monitoring from periodic sampling to continuous surveillance, enabling faster regulatory response to microplastic contamination events.
A Mobile Application to Assist in Reporting and Cleaning Spots of Ocean Litters using Machine Learning
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Researchers developed a mobile application that uses machine learning to help users report and locate ocean litter, aiming to improve community-driven cleanup efforts and generate spatial data on marine plastic pollution.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
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Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
Ecosense: A Youth-Driven IOT and AI-Based Digital Platform for Real-Time Environmental Monitoring and Plastic Waste Detection
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Researchers proposed EcoSense, a youth-developed platform combining IoT environmental sensors, computer vision, and cloud analytics to detect plastic waste in real time and generate location-specific alerts, aiming to bridge monitoring gaps in ecologically sensitive and under-resourced regions.
A Smart Environment Monitoring System for Mitigating Marine Littering
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This study analyzed microplastic contamination in the gills, liver, and muscle tissue of commercially important fish species from a coastal fishery, finding polymer particles primarily concentrated in gill and liver tissues. Polyester and polyethylene were the predominant polymer types identified by FTIR spectroscopy, consistent with sources in the broader coastal environment.
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