Monitoring of Pond Water Quality using Fog Network with Fuzzy Rule based Algorithm

Monitoring the quality of water in ponds is an important matter in the fisheries sector. Monitoring that is still conventional in nature can take a long time and cannot display data in real-time in the current conditions. These problems cannot be ruled out because they involve the life of the fish in them so they can develop well and their production increases. Efforts to monitor water quality in ponds have been carried out online, but unfortunately there are still constraints on internet networks due to the limitations of broadband in interpreting the results of monitoring. Therefore in this study proposed a fog computing network model with fuzzy rule based algorithm for monitoring pond water quality. Inputs from pond water quality are temperature, pH and dissolved oxygen (DO) parameters. The values of these parameters are acquired in real time using sensors on the fog network. These data are then sent and stored in the database on the web server. Data on temperature, pH and DO parameters on the data base are processed by using fuzzy rule based logic. These data used as input on the inference engine. Each input data inferred by the set rules values. The processing results are issued in the form of an information system in the form of a graph that illustrates the current conditions of pond water quality and warning notifications of the condition of the pond water quality monitored.