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Figure 6. Receiver’s serials monitor display Figure 5. Transmitter’s serials monitor display Figure 5 shows the display from the serial monitor of Arduino software when a transmitter sends data dan Figure 6 shows the serial monitor from the Arduino software when a receiver receives data. The measured items are current, power, temperature, weight, date and time, a distance that will be used to detect the fuel oil in the crane, and total working time for the crane. In addition, the signal to noise ratio (SNR) and received signal strength indication (RSSI) values of the LoRa module is also displayed in the serial monitor of the receiver side. SNR and RSSI indicate the signal strength for LoRa.

Figure 6 Receiver’s serials monitor display Figure 5. Transmitter’s serials monitor display Figure 5 shows the display from the serial monitor of Arduino software when a transmitter sends data dan Figure 6 shows the serial monitor from the Arduino software when a receiver receives data. The measured items are current, power, temperature, weight, date and time, a distance that will be used to detect the fuel oil in the crane, and total working time for the crane. In addition, the signal to noise ratio (SNR) and received signal strength indication (RSSI) values of the LoRa module is also displayed in the serial monitor of the receiver side. SNR and RSSI indicate the signal strength for LoRa.

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Figure | shows the block diagram of the system designed. There are two parts which are the transmitter and receiver parts. The sensors will evoke when the crane starts to work. The value of sensors wil be sent to Arduino for processing before being passed to LoRa for transmission. The received data will be sent to the designed database using a Wi-Fi connection from the NodeMCU ESP8266 internet module Finally, the user will be able to view and monitor the crane by running the developed application on the mobile phone. Figure 2 shows the hardware setup for the transmitter side. In this project, ACS712 was used as the current sensor where TS90a servo motor was connected to the sensor to act as the current sensor’s load; to allow current data measurement. An Arduino Nano is used to supply power and program the servo motor to rotate occasionally for every 15 seconds. Besides, the load cell sensor needs a load for measuring the weight data. Thus, an object is added and placed on the load cell sensor to become the weight sensor load. There is also a real time clock (RTC) module used to record the date and time for calculating crane utilization rate. An HC-SR04 Ultrasonic sensor was used to detect the crane’s fuel consumption. DHT22 is used as the temperature sensor for detecting any abnormality of the crane. The crane data mentioned in this project are only simulation data, these data are not tested on the real crane, but the data type can be used to monitor the real crane. For example, the temperature data in this project refers to the temperature of the crane, the weight
Figure | shows the block diagram of the system designed. There are two parts which are the transmitter and receiver parts. The sensors will evoke when the crane starts to work. The value of sensors wil be sent to Arduino for processing before being passed to LoRa for transmission. The received data will be sent to the designed database using a Wi-Fi connection from the NodeMCU ESP8266 internet module Finally, the user will be able to view and monitor the crane by running the developed application on the mobile phone. Figure 2 shows the hardware setup for the transmitter side. In this project, ACS712 was used as the current sensor where TS90a servo motor was connected to the sensor to act as the current sensor’s load; to allow current data measurement. An Arduino Nano is used to supply power and program the servo motor to rotate occasionally for every 15 seconds. Besides, the load cell sensor needs a load for measuring the weight data. Thus, an object is added and placed on the load cell sensor to become the weight sensor load. There is also a real time clock (RTC) module used to record the date and time for calculating crane utilization rate. An HC-SR04 Ultrasonic sensor was used to detect the crane’s fuel consumption. DHT22 is used as the temperature sensor for detecting any abnormality of the crane. The crane data mentioned in this project are only simulation data, these data are not tested on the real crane, but the data type can be used to monitor the real crane. For example, the temperature data in this project refers to the temperature of the crane, the weight
Figure 2. Transmitter side hardware setup data refers to the load of the crane, and data from the ultrasonic sensor refer to the fuel level of the crane. The close-up figure for current and weight sensors and how there were connected is shown in Figure 3. As mentioned earlier, the current sensor needs a load to show readings. Therefore, a servo motor is used tc mimic the real condition. In a real situation, the current sensor will be connected to the crane motor fo current measurement. The weight sensor used in this project can detect weight until 5 kg. A robust anc precise weight sensor needs to be used for measuring real crane data as it carries a bigger load. Figure 4 shows the receiver side of this project. The receiver side is simpler, it is consisting of a LoRa receiver being connected to ESP8266 for Wi-Fi connections.
Figure 2. Transmitter side hardware setup data refers to the load of the crane, and data from the ultrasonic sensor refer to the fuel level of the crane. The close-up figure for current and weight sensors and how there were connected is shown in Figure 3. As mentioned earlier, the current sensor needs a load to show readings. Therefore, a servo motor is used tc mimic the real condition. In a real situation, the current sensor will be connected to the crane motor fo current measurement. The weight sensor used in this project can detect weight until 5 kg. A robust anc precise weight sensor needs to be used for measuring real crane data as it carries a bigger load. Figure 4 shows the receiver side of this project. The receiver side is simpler, it is consisting of a LoRa receiver being connected to ESP8266 for Wi-Fi connections.
Figure 3. Current and weight sensor setup
Figure 3. Current and weight sensor setup
Figure 4. Receiver side hardware setup
Figure 4. Receiver side hardware setup
Figure 10. Display after “Past Record” icon was Fi clicked Figure 8. On launch interface
Figure 10. Display after “Past Record” icon was Fi clicked Figure 8. On launch interface
Figure 11. Display after data was entered in specific format and “Submit Date” icon was clicked Figure 9. Display after icon “Realtime monitoring” was clicked
Figure 11. Display after data was entered in specific format and “Submit Date” icon was clicked Figure 9. Display after icon “Realtime monitoring” was clicked
Crane monitoring system based on internet of things using long range (Ng Wen Jun) Figure 13. Zoom in option Figure 12. Spinner’s sensor option
Crane monitoring system based on internet of things using long range (Ng Wen Jun) Figure 13. Zoom in option Figure 12. Spinner’s sensor option
work easier. Besides that, the crane’s temperature is observed for its motor and hydraulic part. If the temperature of the motor keeps increasing, it may cause the system of the crane to fail and stop working normally. Figure 18 shows the weight data in the per hour graph view. In this project, 5 grams of load is being used although the system able to detect up to 5 kg. The weight sensors allow the user to monitor and control the maximum capacity that a crane can lift. Whenever a crane is overloaded, it causes irreversible damage and may cause hazards. The most common case will be the drop of the load or crane is swinging and hard to be controlled.
work easier. Besides that, the crane’s temperature is observed for its motor and hydraulic part. If the temperature of the motor keeps increasing, it may cause the system of the crane to fail and stop working normally. Figure 18 shows the weight data in the per hour graph view. In this project, 5 grams of load is being used although the system able to detect up to 5 kg. The weight sensors allow the user to monitor and control the maximum capacity that a crane can lift. Whenever a crane is overloaded, it causes irreversible damage and may cause hazards. The most common case will be the drop of the load or crane is swinging and hard to be controlled.
Figure 16. Power per hour graph display Figure 17. Temperature per hour graph display
Figure 16. Power per hour graph display Figure 17. Temperature per hour graph display
Figure 18. Weight per hour graph display
Figure 18. Weight per hour graph display
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Computer ScienceReal Time Computing
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