Use of IoT and Artificial Intelligence (AI) in the mining industry for two critical areas

Use of IoT and Artificial Intelligence (AI) in the mining industry for two critical areas:

1. Fleet Management & Optimization
2. Video Surveillance & Security Monitoring

These technologies help mining companies increase operational efficiency, reduce costs, and enhance safety across their vehicle fleet and remote sites.

🚚 1. IoT & AI in Fleet Management & Optimization in Mining Industry

✅ Overview

Mining fleets consist of haul trucks, loaders, bulldozers, graders, water carts, light vehicles, and drills, which must be monitored, scheduled, and maintained effectively. IoT and AI provide real-time operational intelligence and predictive optimization of these mobile assets.

📡 A. IoT Contributions in Fleet Management

1. Vehicle Telemetry & Tracking

• GNSS (GPS + GLONASS) modules track real-time vehicle position, speed, heading, and route.
• Onboard telematics units collect data from engine control modules (via CAN bus):
  • Fuel level
  • Engine temperature
  • Idling time
  • Load per trip
  • Brake status

2. Sensor Integration

• Load sensors measure the payload for each haul.
• Tire Pressure Monitoring Systems (TPMS) detect leaks or inflation issues.
• Fuel flow meters track actual consumption to identify theft or inefficiency.

3. Communication Networks

• Data transmitted via private LTE, satellite, or mesh networks in remote mines.
• Gateways or mobile edge devices pre-process telemetry data before sending to the cloud.

🤖 B. AI Contributions in Fleet Optimization

1. Route Optimization

• AI algorithms (Dijkstra, A*, genetic algorithms) find the shortest and most fuel-efficient haulage routes.
• Live road conditions, inclines, and congestion are factored in for real-time rerouting.

2. Cycle Time Optimization

• Machine learning models analyze loading → hauling → dumping → return
• AI identifies bottlenecks, underutilized trucks, or idle shovels to optimize match capacity.

3. Fuel Usage Prediction

• AI forecasts fuel usage per shift based on:
  • Load size
  • Route topography
  • Operator behavior
• Helps identify excessive fuel usage due to aggressive driving or idling.

4. Predictive Maintenance of Fleet

• AI models trained on sensor and maintenance data forecast:
  • Engine failures
  • Transmission wear
  • Brake system faults
• Automates work order generation in the CMMS.

🧠 C. Benefits of IoT + AI in Fleet Management

Benefit Area	Improvement
Fuel Efficiency	10–15% cost reduction
Fleet Utilization	+20% increase in productivity
Downtime	Reduced by 25–40% via predictive maintenance
Safety	Improved by route compliance & fatigue detection
Manual Intervention	Drastically reduced with automation

📊 Fleet Management Architecture Diagram

🎥 2. IoT & AI in Video Surveillance in Mining

✅ Overview

Mining sites require extensive 24/7 surveillance for worker safety, equipment security, environmental monitoring, and compliance. Traditional CCTV is passive. IoT-enabled smart video surveillance systems with AI computer vision are proactive and intelligent.

📡 A. IoT Contributions in Video Surveillance

1. Smart Cameras

• IP cameras with thermal, infrared, night vision, and pan-tilt-zoom (PTZ) capabilities.
• Connected via PoE, 4G/5G, or fiber optics to local NVRs or cloud storage.

2. Edge Video Processing

• Onboard processing in cameras or edge boxes runs basic analytics (motion detection, object counting).
• Reduces latency and bandwidth usage by processing footage locally before transmitting.

3. Environmental Integration

• Video feeds combined with IoT environmental data (e.g., gas leaks, temperature spikes, unauthorized access) to provide situational awareness.

🤖 B. AI Contributions in Video Surveillance

1. Intrusion Detection & Perimeter Security

• AI computer vision models (YOLOv7, OpenCV, DeepStream) detect:
  • Unauthorized personnel in restricted zones
  • Vehicle intrusions at odd hours
  • Fence breaches or perimeter violations

2. PPE & Safety Compliance Monitoring

• CV models identify:
  • Helmet use
  • Reflective vests
  • Safety boots
• Real-time alerts sent to supervisors if safety gear is missing.

3. Operator Behavior Monitoring

• Fatigue detection using facial landmarks (eye blinking, yawning).
• Detection of unsafe behaviors (phone use, smoking near flammable storage).

4. Fire, Smoke, and Spill Detection

• Thermal imaging + AI models identify:
  • Spontaneous combustion
  • Leaking tanks or chemical spills
• Automated escalation: sirens, SMS alerts, camera zoom on the hazard.

🧠 C. Benefits of Smart Surveillance

Use Case	Benefit
Intrusion Detection	24/7 real-time alerts, reduced security personnel
Safety Compliance	Continuous PPE monitoring, better incident control
Theft Prevention	Visual logs + automated response
Emergency Response	Faster detection of fire/gas leaks with location tagging
Data Storage	Reduced by filtering only relevant events

📊 Smart Surveillance System Architecture

✅ Combined Applications: Fleet & Surveillance Synergy

• Cameras on trucks monitor loading activity, operator fatigue, and unsafe behavior.
• Video analytics cross-referenced with fuel consumption, trip logs, and geolocation to detect:
  • Ghost trips
  • Misuse of vehicles
  • Material theft

🔄 Summary of Key Benefits

Domain	IoT & AI Value
Fleet Efficiency	Optimized cycle times, fuel savings
Safety	PPE enforcement, route compliance
Asset Protection	Theft alerts, camera-based evidence
Environmental Monitoring	Visual confirmation of spills or damage
Labor Productivity	Reduced supervision effort
Cost Control	Less idling, lower maintenance, fewer incidents