| dc.description.abstract | This research presents an improved and more effective approach for data acquisition of recirculation aquaculture system (RAS). In the previous research, the system uses manual methods to take the important data from RAS and it wastes the time because
manual system uses human compare to computer. It is also gets late response from the fish farmer if the data is not in the good condition. As a result, fog computing technology is applied to overcome all these problems and acts as advance data
acquisition system to keep data safely by sharing the processed data in fog computing for every tanks and analyze the data to make an accurate control/decision in the real time. Besides, open source technology plus embedded system based has been integrated for this research because its benefits such as small size, low cost, lightweight, portable,
high efficiency and low power consumption. This research has achieved the objectives which are design and develop data collecting system, data processing system using fog computing for RAS and validate the system. The data collecting system for RAS (RaspDAQ) is developed by connecting Raspberry Pi 3 to temperature sensor (LM35DT) using analogue digital converter (ADC) MCP3002, water level sensor (HCSR04), Rpi camera module, LEDs and buzzer. Software and program are built using
Python and Apache server to run every functions of RaspDAQ. Two RaspDAQ are used in this research which are RaspDAQ1 and RaspDAQ2. While third Raspberry Pi 3 is setup as data processing and server system (RaspFog). Raspfog uses PHP, Apache and MySQL database. Both RaspDAQ and RaspFog are based on Raspbian operating system. After that, RaspDAQ1 and RaspDAQ2 are connected to RaspFog using WiFi technology to send sensors data in real time. The received data are stored and plotted using Highcharts.com graph. Both RaspDAQ, RaspFog have been tested and validated. At the same time, users can see the graph output in the real time for temperature, water level sensor and real condition using Rpi camera module of RaspDAQ1 and RaspDAQ2 by browsing RaspFog website. Finally, fog computing technology has been implemented successfully to RAS in this research. | en_US |
