Abdul Jalil

This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.

Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.

This study aims to develop a device that can assist the mobility of paralyzed patients, enabling them to communicate with family and caregivers by integrating robot vision and virtual reality (VR). The method used to connect audio and visual data communication between robot vision and VR is by utilizing the robot operating system (ROS2) middleware communication node through topics over a wireless network. In this research, paralyzed individuals can maneuver based on the movement direction of robot vision, which is remotely controlled via a joystick through Bluetooth communication. The input devices used in this system include a camera, microphone, joystick, and ultrasonic sensors. The processing part uses a Raspberry Pi as the data processing center, and the output includes a DC motor, servo motor, speaker, 5-inch monitor, and headset. The results indicate that the integration of robot vision and VR can assist paralyzed individuals in communicating with family or caregivers at distances of up to 10 meters. This is due to the maximum joystick control range for moving the robot via Bluetooth communication being 10 meters. Furthermore, this study shows that the use of robot vision and VR can improve paralyzed patients’ motivation, supporting the medical field in patient care.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish–subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish-subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

This study aims to design a digital kWh-meter prototype to top-up the electricity pulse by automatically using relay modules based on Short Message Service (SMS) and Arduino Uno. The utilization of 12 relay modules to substitute the keypad input function in the digital kWh meter is our basic idea in this study. The method we used to replace the keypad input function with the relay module is based on the integration between the circuit path in the keypad board and the relay module as an electric switch that can activate when the relay gets a trigger from the Arduino Uno. In this study, when the user wants to charge the electric pulse, the user will send the voucher number to the GSM SIM900A module via SMS, then it will be processed to the Arduino Uno. Then Arduino Uno will trigger the relay to be activated so that it can automatically fill the voucher number to the digital kWh-meter. This study result is the success of relay modules can substitute the function of keypad input to fill the voucher pulse number to the digital kWh-meter through SMS with the successful voucher number filling up to 98%. The usefulness of the relay module to change the keypad input function on the digital kWh meter is our original idea for this study.

This study aims is to build a home security system based on object motion detection using the Robot Operating System 2 (ROS2) and the Raspberry Pi. ROS2 in this study was used to read and process the camera data and to control the buzzer sound. At the same time, the Raspberry Pi hardware functioned to run ROS2 using the Linux Ubuntu 18.04 operating system. The camera function to read the image and video data could be developed for the input device to control the home security systems based on object motion detection. The method used to detect an object motion was Binary-Image Comparison (BIC). This method works by comparing the value of the binary image object with the binary master and using it as a decision-making algorithm when the camera detected the object movement based on the object colors. The object colors that were detected in this study were red, yellow, green, and blue. Each object color was processed using the OpenCV library in the ROS2 node service. After that, all of the nodes communicated through topics to communicating and exchanging the message data. This study has successfully developed a prototype that can generate a buzzer sound warning to the user when the camera detected the object motion based on the object color.