Kalman Filter-Enhanced Data Aggregation in LoRaWAN-Based IoT Framework for Aquaculture Monitoring in Sargassum sp. Cultivation

This study presents a LoRaWAN-based IoT framework for robust data aggregation in Sargassum sp. cultivation, integrating multi-sensor monitoring and Kalman filter-based data enhancement. The system employs water quality sensors—including temperature, salinity, light intensity, dissolved oxygen, total dissolved solids, and pH—deployed in 6 out of 14 cultivation containers. Sensor data are transmitted via LoRaWAN to The Things Network (TTN) and processed through an MQTT-based pipeline in Node-RED before visualization in ThingSpeak. The Kalman filter is applied to improve data accuracy and detect faulty sensor readings, ensuring reliable aggregation of environmental parameters. Experimental results demonstrate that this approach effectively maintains optimal cultivation conditions, reducing ecological risks such as eutrophication and improving Sargassum sp. growth monitoring. Findings indicate that balanced light intensity plays a crucial role in photosynthesis, with optimally exposed containers exhibiting the highest survival rates and biomass. However, nutrient supplementation showed limited impact due to uneven distribution, highlighting the need for improved delivery systems. By combining real-time monitoring with advanced data processing, this framework enhances decision-making in sustainable aquaculture, demonstrating the potential of LoRaWAN and Kalman filter-based methodologies for environmental monitoring and resource management.