Sensor Data Fusion for Monitoring Water Quality Toward Sustainable Freshwater Fisheries

The use of wireless sensors is increasing day by day. Different types of wireless sensors are being used in fisheries sectors to monitor the water quality, growth of the fish, and health of the fish. Due to a brupt changes in water quality parameters, the rapid outbreak of fish disease has become a significant constraint for this sector's sustainability. Development of an early monitoring system of fish culture parameters through high resilience and efficiency wireless sensor networks (WSN) effectively assess water quality regulators instantly and thus take proper actions for sustainable management of freshwater resources. However, current observation systems only consider the data from a single sensor. We designed a data fusion model using Dempster-Shafer theory (DST) to fuse the monitoring sensor data from different sensors to calculate the fish's sustainable environment. Moreover, we evaluated our monitoring system results for different scenarios using the standard performance metrics, i.e., specificity, sensitivity, a ccuracy, and F-Score were calculated using the True Positives (TP), False Positive (LIP), True Negative (TN), and False Negative (LIN) values. Our model's finding shows that fusing data from different sensors provide a more accurate result for monitoring the water's sustainability.