CargoSensePro

- Automated Train Container Monitoring System

Wokwi Simulation :

- https://wokwi.com/projects/388267915240027137

Thingspeak Server Public View :

- https://thingspeak.com/channels/2035354

Deployed Website :

- https://atharvapawar456.github.io/CargoSensePro/

Mobile Hotspot Setting:

- ssid = "CargoPro"
- Pass = "Cargo@123"

UART Communication between ESP32-Master and ESP32-Slave

- ESP32 (Master) : Tx:Yellow  ; Rx:Green
- ESP32 (Slave ) : Rx:Yellow  ; Tx:Green

CargoSensePro - Dashboard

CargoSensePro - Project Details

CargoSensePro - Contact

Abstract

This project aims to automatically detect and record train container parameters such as weight, content height, and container ID at different stations using an ESP-32 microcontroller, HC-SR04 ultrasonic sensor, load cells, RFID, servo motor, and OLED display. The collected data is uploaded to a ThingSpeak server and rendered on an HTML website for real-time monitoring.

Problem Statement

Current train container monitoring systems lack automation, leading to manual errors and delays in identifying data loss during transit. This project addresses these issues by providing an automated solution for monitoring and recording container parameters.

Description

The system comprises ESP-32 microcontroller, HC-SR04 ultrasonic sensor for height measurement, two 10kg load cells for weight measurement, RFID for container identification, a servo motor for control, and an OLED display for real-time information. The collected data is sent to a ThingSpeak server and visualized on an HTML website.

Approach

Station 1: RFID identifies the container, load cells measure weight, and ultrasonic sensor measures height. Data is sent to ThingSpeak. Transit: Container moves to Station 2.
Station 2: RFID identifies the container, load cells and ultrasonic sensor measure parameters. Data is compared with Station 1. Discrepancies indicate data loss.

Advantage

Automation reduces manual errors.
Real-time monitoring of container parameters.
Quick identification of data loss during transit.

Limitation

Dependency on RFID for container identification.
Accuracy of measurements may be affected by external factors.

Future Scope

Integration with GPS for location tracking.
Implementation of machine learning for predictive analysis.
Enhanced security features for RFID.

Block Diagram

Getting Started

Clone the repository.
Install required libraries (list them in the document).
Connect the components as per the provided circuit diagram.
Upload the code to the ESP-32.
Monitor the data on the ThingSpeak channel and access the HTML website for real-time visualization.

Contributors

List the contributors and their roles in the project.

License

Specify the project's license.

Acknowledgements

Acknowledge any third-party tools, libraries, or resources used in the project.

Api

https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=9&field2=8.2&field3=10.3 https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=9&field2=8.1&field3=10.1

https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=5&field2=8.4&field3=10.3 https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=5&field2=8.2&field3=10.5

https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=11&field2=8.9&field3=10.9 https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=11&field2=8.2&field3=10.5

https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=20&field2=8.9&field3=10.9 https://api.thingspeak.com/update?api_key=K8CH7XH11OFPA1WG&field1=20&field2=8.2&field3=10.5

AtharvaPawar456 / CargoSensePro