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Cloud Computing-Based Advanced Slope Monitoring System for Opencast Coal Mines

EasyChair Preprint no. 9716

11 pagesDate: February 15, 2023


Over the past few years, the Internet of Things (IoT) evolved from merely a concept into a growing reality. The Internet of Things and wireless sensor networks with cloud applications are emerging technologies that provide real-time slope monitoring in opencast mines.  Wireless Sensor Networks are the fast and more efficient method of slope monitoring compared to many other methods. Development of an advanced slope monitoring system that works on real-time slope data analysis and is equipped with an early warning system against slope failures is crucial for ensuring the safety of manpower and machinery.

This paper introduces an advanced slope monitoring system with an Arduino microcontroller, wireless sensors to measure specific slope parameters and LoRa E32 433 Mhz radio module to transmit sensor data to the receiver. On the receiving side, the Node MCU microcontroller sends sensor data to the Thingspeak cloud platform, which performs data collection, analysis, and early alerts.  It is possible to predict slope behavior by monitoring specific slope parameters such as moisture content, intensity of blast vibration, and slope displacement, which are the parameters selected for slope monitoring in this study and measured using respective sensors. The proposed system is installed at an opencast coal mine with two nodes of moisture, vibration, displacement sensors and LoRa RF module.

Keyphrases: Arduino microcontroller, LoRa Node MCU, Python, Realtime slope monitoring, ThingSpeak, Wireless Sensor Networks

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sathish Kumar Mittapally and M Shyam Sunder and Ram Chandar Karra},
  title = {Cloud Computing-Based Advanced Slope Monitoring System for Opencast Coal Mines},
  howpublished = {EasyChair Preprint no. 9716},

  year = {EasyChair, 2023}}
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