DEVELOPMENT OF AN IOT-BASED SMART MONITORING SYSTEM FOR REAL-TIME MARINE POLLUTANT TRACKING

This article presents the design and implementation of a novel IoT-based smart monitoring system for real-time marine pollutant tracking. The proposed system integrates multi-parameter water quality sensing (temperature, pH, turbidity, GPS) with AI-powered floating debris detection using a TensorRT-optimized YOLOv8 model deployed on the NVIDIA Jetson Orin Nano. Data is transmitted via LoRa to a central gateway and visualized through a Blynk IoT dashboard, enabling remote monitoring with geotagged detections. Field validation demonstrated accurate sensor readings (~1% deviation), stable real-time object detection at 30 FPS with 50–60 ms latency, and successful integration of edge AI with long-range IoT communication. The study highlights the feasibility of low-cost, scalable, and autonomous monitoring solutions for environmental sustainability, with potential applications in pollution mapping, aquaculture management, and disaster preparedness.