WHITE PAPER
Smart Mining with
Private 5G Technology:
Revolutionizing Industry
Authors:
Dr. Pradyumna Ku Patra, NIST University, India
Mr. Manan Shah, Celona
Smart Mining with Private 5G Technology | 1
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Abstract
Private 5G technology has the potential to revolutionize the mining industry. Over time, private 5G
technology is expected to increase mining operations’ sustainability, safety, and efficiency, which will
enhance the operator’s return on investment. Real-time communication and remote monitoring will
be possible thanks to private 5G technology, which will also improve the automation, measurement,
and overall productivity of mining operations. In this white paper the authors address how mining
can be modernized by adopting private 5G technology which can resolve current challenges,
streamline processes, and open up new growth opportunities.
Table of Contents
Introduction 3
Current Status of the Mining Industry 4
Odisha Addresses 5 Key Challenges to Enhance Mining Operations 6
New Possibilities with Private 5G 10
Methodology 11
AI & ML: Shaping the Future of Autonomous Mobility and 6G Connectivity 13
Machine Learning Model Selection 15
Benefits/Expected Outcome 17
Conclusion 18
Acknowledgements 19
References 19
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Introduction
In India, there has been a significant focus on the need
for adoption of new technology for the mining industry
for key operations like drilling, blasting, excavation, and
ore transportation. In this transition, organizational and
environmental factors also need to be taken into account.
Using outdated technologies decreases efficiency
and makes ecological protection more challenging.
Furthermore, India’s social and cultural dynamics can
complicate the introduction of automation and
modern technologies.
Overcoming these obstacles requires cooperation
among mining companies, service providers, research
institutions, and educational bodies. A detailed strategy
is necessary, focusing on developing new technologies,
enhancing tools and data analysis, revising educational
programs, and creating government programs and
initiatives to modernize India’s mining sector.
Historically, the mining industry has been struggling
with sustainability, safety, and efficiency. The sector
must adopt new technology as the demand for minerals
is rapidly increasing on a global scale. The private 5G
network is a revolutionary solution that offers
lightning-fast, reliable connections, robust security, and
the capacity to link thousands of Industrial IoT devices. It
facilitates so-called “smart mining” by enabling artificial
intelligence (AI) and machine learning (ML) to enhance
data transmission rates, facilitate real-time data
processing and activate remote-control and monitoring
capabilities, resulting in real-time insights and flexibility
in digital supply chains and operations. Private 5G is a
crucial technology driving the industry’s shift to
digital transformation.
This white paper proposes a framework for implementing
a private 5G network to address challenges in the mining
sector by providing high-speed, low-latency connectivity,
and will demonstrate how private 5G technology is
expected to revolutionize the mining industry. Private
5G enables quality of service (QoS) for seamless
communication between surface and underground
operations, facilitating unmanned mining and improving
safety. Its high data rates, low latency, and advanced
Radio Access Network (RAN) make it suitable for a variety
of mining applications, including block cave mining. Figure 1: Underground Mine and Surface Mine
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Current Status of the Mining Industry
The mining sector has long faced several obstacles that impede their growth and operational efficiency:
Safety Concerns
The mining industry is one of the sectors where workers are most likely to encounter a hazardous work
environment. Potential threats include cave-ins, explosions, toxic air, and equipment accidents, leading to
serious injuries and fatalities, as well as long-term health issues from dust and chemical exposure. Mining
companies are constantly seeking ways to improve safety conditions.
Operational Efficiency
The traditional mining methods frequently lack efficiency because of old networks which depend on
antiquated technologies and the corresponding equipment that hinder productivity as well as hinder
real-time decision-making. Much of the networking equipment in use is based on turn of the century 2.5G
and 3G technology, making it difficult to monitor performance and promptly address problems.
Environmental impact: The effects on the environment of mining are substantial, often leading to increased
carbon emissions, habitat destruction, and water contamination.
Data management: Data management is a complex task that requires intelligent solutions for real-time
information analysis and action, especially considering the vast volumes of data generated by modern
mining operations.
These challenges can be seen in Odisha. While it contributes approximately 6% to Odisha’s GDP, the
economic advantages of mining are not as significant as those provided by the manufacturing sector.
To address these issues and promote responsible mining practices, it is essential to enforce regulations
rigorously and conduct regular geospatial monitoring.
Figure 2: Existing i3MMS Technology in Odisha Mining
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In resource-rich Odisha, the government has implemented an Integrated Intelligent Mineral Management
System (i3MMS) to improve the management and security of mining operations. Satellites, drones, GPS,
and RFID are being integrated into i3MMS for monitoring and tracking minerals, which improves safety,
productivity, and environmental management. Satellite monitoring can be used to detect illegal mining.
Drone images provide detailed data to enable land-use studies and environmental impact assessments.
GPS tracking is used to prevent illegal transportation of material and streamline mineral income
administration. IoT sensors are being deployed to enhance safety. Automated weigh bridges ensure
regulatory compliance. Relying on legacy 2.5G/3G technology for connectivity may hinder the ability to
achieve the desired results from these modern methods.
Figure 3: Odisha i3MMS based on 2.5G and 3G technology
The dangerous and inaccessible nature of
underground mines necessitates a move to
private 5G technology for improved safety and
operational efficiency. An important 5G feature
is ultra-reliable low latency (URLLC) connectivity,
which is essential for real-time automation like
machine-to-IoT sensor (machine-to-machine
or M2M) applications. URLLC ensures that M2M
connections used for operating, monitoring
and managing equipment can handle the high
data rates demanded by automated machinery
like Autonomous Haulage Systems (AHS). AHSs
are autonomous trucks used in mining and are
vulnerable to cyber-attacks, underscoring the
critical importance of secure connectivity in
these environments. The extreme underground
circumstances can make wireless technology
implementation challenging. The goal of this
white paper is to present the proof-of-concept
technologies that are best suited to specialized
jobs in this environment, including automation, Figure 4: Autonomous Haulage Systems (AHS)
tracking, and long-range monitoring.
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Another advantage of 5G technology over earlier network generations is its ability to support the increasing
quantity of data generated by automation. 5G produces packets transmitted in the user and control plane
over the air interface with remarkable efficiency. The system utilizes flexible numerology (μ=0,1,2,3,4), which
allows for varying subcarrier spacings tailored to different use cases, thereby optimizing both capacity and
latency. Furthermore, implementing adaptive networks supporting up to 256-QAM (Quadrature Amplitude
Modulation) as needed will achieve higher data rates, enhancing spectral efficiency by encoding more bits
per symbol and improving overall network performance.
Odisha Addresses 5 Key Challenges to Enhance Mining Operations
Odisha has implemented a comprehensive suite of technology-driven solutions to modernize its mining
sector, improve transparency, ensure regulatory compliance, and promote sustainable practices. These
systems cover the entire mining lifecycle, from exploration and extraction to transportation and environmental
monitoring. Here’s a breakdown of the key technologies in use:
Figure 5: Transformative Impact of Private 5G on Smart Mining and Logistic
Surveillance and Monitoring
Satellite-Based Monitoring and Surveillance Observation through drones: Drones provide air
System (SATMMS): This system developed by images and videos in hard-to-reach areas in real
ORSAC uses satellite images to monitor production time to provide economically effective solutions for
boundaries, detect illegal activities, and change the land research and to ensure compliance with terrain
level of land use including forest cutting. monitoring, environmental impact assessments,
extraction limits and operational progress.
Settings through GPS: GPS is installed on mining
vehicles and monitoring equipment to prevent
unauthorized production and illegal transportation
and of minerals.
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Mineral Tracking and Logistics
Radio-Frequency Identification (RFID): RFID the pit they are tracked on their journey to storage
technology is deployed in mining vehicles and and shipment. The system is designed to improve
mineral containers to enable real-time tracking transparency and ensure accountability throughout
of mineral movements. This promotes logistical the entire process.
transparency and halts illegal activities by ensuring Automated weighbridges: These automated weight
that minerals are transmitted via approved routes. measurement systems for trucks, which are outfitted
This technology helps to fight illegal mining by with integrated cameras and sensors, significantly
increasing transparency and ensuring accountability address challenges associated with fraudulent
throughout the process. RFID not only helps prevent practices like overloading and enhance the reliability
overloading, but also reduces the possibility of of the datasets related to extraction and
fraudulent activity, ensuring that the extracted and dispatch operations.
shipped datasets match perfectly. It also optimizes
track routes in real time to ensure production targets Fleet management and dispatch system: This
are reached efficiently and on time. system autonomously schedules mining equipment in
accordance with production demands and allocates
i3MMS Online mineral transport and shipping tasks to mining machinery, thereby optimizing the
management system: This all-in-one online platform utilization of mining devices in real-time.
follows every stage of mineral production in
real-time. The moment minerals are extracted from
Operational Efficiency and Safety
Mine Management Systems (MMS): Centralized Excavator and payload monitoring: AI-powered
MMSs digitally monitor and control mining systems monitor excavator tooth condition and
operations – from ore extraction to assess shovel loads, reducing downtime and
transportation – enabling real-time data optimizing equipment utilization.
integration, resource tracking, enhanced Real-time analytics: Integrated data platforms
efficiency, reduced human intervention, and collect and analyze performance data, enabling
improved compliance. predictive maintenance and minimizing
IoT-based sensors: Sensors on mining equipment unexpected equipment failures.
are used to monitor temperature, gas emissions, Remote access and control: These systems allow
vibrations, and equipment health, enhancing safety remote oversight of mining operations from
(e.g., gas leak detection) and enabling preventive anywhere, with vernacular language support for
maintenance to avoid equipment failure. improved accessibility across diverse teams.
Automated drilling and blasting control: This
technology improves the precision and safety
of drilling and blasting operations, enhancing
efficiency and reducing risks.
Environmental Monitoring and Compliance
The Odisha State Pollution Control Board (OSPCB) is a leader in environmental stewardship, utilizing state-of-
the-art automated stations to monitor critical environmental factors in real-time. This includes monitoring the
integrity of both air and water, as well as noise pollution and dust levels. The OSPCB department utilizes IoT
components to monitor air and water quality, detect hazardous gases, and locate industrial emissions in order
to ensure ecological equilibrium and adherence to environmental regulations.
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Security and Access Control
Smart helmets and biometrics are deployed to ensure that only authorized personnel enter restricted areas.
The integrated biometric dataset keeps a record of face, fingerprint, and iris scanning of employees and the
smart helmet keeps track of location. A centralized orchestrator is used to manage and act on the data. This
enhances operational security and worker safety.
Odisha’s goal is to establish a mining sector that is more sustainable, efficient, and transparent, while
also minimizing environmental impact and optimizing resource utilization through the integration of these
advanced technologies. In this white paper, the authors propose a whole network architecture and logistics
management to support this smart mining goal to boost efficiency, productivity, strategic operations, and
advanced logistic management.
Figure 6: Transformative Architecture of Private 5G Smart Mining and Logistics
As seen in Figure 6, private 5G provides a whole network architecture to support smart mining and logistics
with modern wireless connectivity. Private 5G technology delivers advanced protocols like URLLC, Software-
defined networking (SDN), network slicing, and enforcement of network within the network to securely meet
key performance requirements. Each layer will contain their specific roles and policies, using smart logic
to integrate work and data flows within a centralized system. The arrows in Figure 6 show data flows and
interactions between each node and towards the centralized 5G Multimedia Messaging Service (MMS). This
approach leverages the security, speed and low-latency of 5G to drive modern automation applications that
will provide visibility, enhance mining productivity, and support sustainability.
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Figure 7: Grading Coal with IoT Sensors
Figure 7 shows a concrete example of how this all comes together in a specific application – using IoT
sensors for grading coal. IoT sensors can be deployed at the mining site or in the plant to capture
real-time data about coal properties, such as moisture content, calorific value, ash content, sulphur content,
and density, greatly reducing processing time. Determining the grade of coal using IoT sensors running on
private 5G can take advantage of several advanced technologies enabled by the high speed, low latency,
and massive connectivity of 5G.
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New Possibilities with Private 5G
Figure 8: Transformative Impact of 5G on Smart Mining and Logistic Industry
Private 5G technology will open new possibilities for the mining industry, including:
Autonomous Mining Operations
Driverless trucks, drones, and robots will be able to work in hazardous conditions without the need for
human intervention, improving safety and efficiency.
Environmental Monitoring
AI models can be applied to real-time environmental data for rapid response to improve
ecological protection.
Logistics and Supply Chain
RFID and ruggedized scanners can be deployed for real-time tracking that feeds data into smart inventory
management systems, increasing operational efficiency and reducing loss.
Real-time Data Analytics
IoT sensors for applications such as coal grading will generate real-time data, which can be analyzed
instantly using AI models to optimize production, improve resource allocation, and reduce
environmental impacts.
Enhanced Safety
With 5G’s low-latency capabilities, sensors can be deployed to detect and respond to gas leaks and other
hazards, and self-driving vehicles and mining operations can be remotely monitored and controlled from
anywhere in the world, enabling quicker responses to issues and reducing the need for on-site personnel in
hazardous environments.
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Methodology
Figure 9 represents the basic smart mining network architecture built on the Celona 5G LAN:
Figure 9: Basic Private 5G Network Architecture for Smart Mining
The successful implementation of private 5G networks in smart mining requires a well-structured deployment
framework that ensures seamless connectivity, minimal latency, and efficient data processing. Deployment
follows a simple yet structured methodology:
1. Assessment and planning: Assess the current mining infrastructure and identify areas where private
5G can provide the most benefit. Plan for the installation of private 5G equipment, including 5G access
points and IoT devices.
2. Pilot testing: Conduct pilot tests in select areas of the mining operation to validate the technology’s
effectiveness and refine systems based on lessons learned.
3. Integration with existing systems: Integrate the private 5G network with existing automation and data
analytics systems to ensure seamless operation.
4. Deployment and scaling: Gradually scale up private 5G deployment across the entire operation,
ensuring that all systems are fully integrated and optimized.
5. Continuous monitoring and improvement: Continuously monitor system performance and make
adjustments based on real-time data to optimize mining operations.
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Figure 10 represents some of the most common use cases and IoT devices deployed on the Celona 5G LAN
for smart mining today.
Figure 10: End-to-end Solution for Smart Mining
The vast amounts of data generated by these IoT sensors can be transmitted to the cloud or can be kept on
on-site for processing. Private 5G’s ultra-low latency and high-bandwidth connectivity ensures seamless data
transmission, even in areas with high sensor density. Large volumes of data can be processed and analyzed
in real time, enabling immediate insights and decision-making, especially when AI/ML models are applied.
Private 5G also enables edge computing, where data processing occurs at the edge of the network, further
reducing latency and improving overall system efficiency.
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AI & ML: Shaping the Future of Autonomous Mobility and
6G Connectivity
Figure 11: Architecture of Central Hub Integrating Edge/Cloud AI and Third-party APIs for Autonomous
Network Orchestration
Celona 5G LAN integrates AI/ML capabilities directly into its network orchestration dashboard, enabling
advanced automation and real-time analytics. As the industry progresses toward 6G, Celona’s architecture
is uniquely positioned to support next-generation AI/ML model integration. The platform’s open APIs and
edge-native design allow seamless interfacing with optimized AI/ML frameworks, accelerating data
processing, reducing latency, and enabling deep learning applications at scale.
Following are the key components represented in the Figure 11 architecture diagram:
APIs & Data Pipelines Ecosystem
• Flow: 5G telemetry → Kafka/Kinesis → AI models • SA RAN: AI-driven RAN optimization
(anomaly detection, QoS) → Optimized outputs • AI Marketplaces: Certified OEM vendors
• Enhancements: NLP-driven network analysis, successfully interface for model deployment
dynamic API prioritization
Security
Edge-to-Cloud AI • Federated Learning: Privacy-preserving AI
• Edge: TinyML on Jetson/Qualcomm for for healthcare/IIoT
congestion detection • Explainability: SHAP/LIME for
• Cloud: SageMaker/Vertex AI for traffic prediction transparent decisions
• Enhancements: GNNs for traffic analysis, and 6G Research
edge data deduplication
• JCAS (AI for sensing/communications),
Network Slicing & Digital Twins Holographic MIMO, Semantic AI
• AI Slicing: Reinforcement learning for autonomous
vehicles/AR/VR
• Digital Twin: Simulates 6G (terahertz, URLLC,
mMTC, eMBB) via NetSim/MATLAB/Ansys
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Several advanced technologies contribute to the success of smart mining by enabling real-time monitoring,
automated decision-making, and efficient smart mining management. These key technologies include:
Internet of Things (IoT)
A network of smart sensors and drones continuously collect real-time environmental data, including soil
moisture, temperature, and humidity. These data points provide a granular understanding of field conditions,
allowing staff to make informed decisions about the current situation of the site.
Artificial Intelligence (AI)
AI-powered models analyze incoming sensor data and generate predictive insights to optimize operations.
By identifying such things as patterns, textures, acidification, and weather, AI helps anticipate and measure
the grade of the ore and implement data-driven interventions for transparency and accountability.
Big Data Analytics
The ability to process and interpret large volumes of mineral data enables mine operators to optimize
resource allocation, apply effective rotation, and yield predictions. Machine learning techniques extract
valuable insights from historical and real-time datasets, improving mining efficiency and sustainability.
Drones and Robotics
Autonomous drones monitor mining conditions by capturing high-resolution aerial imagery and sensor data.
Meanwhile, robotic mining equipment automates labour-intensive processes, improving operational efficiency
and reducing human risk and workload.
Edge Computing and Private 5G
Instead of relying on cloud-based processing, edge computing allows data to be processed locally, reducing
latency and enabling instant AI-driven decision-making. Private 5G networks provide the high-speed,
low-latency communication infrastructure necessary for real-time smart mining automation.
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Machine Learning Model Selection
According to the desired throughput results, one can select the models that balance inference speed and
computational efficiency in constrained network environments. The following models were chosen based on
their ability to operate effectively within the private 5G architecture:
Random Forest Classifier: An ensemble learning model that reduces overfitting and improves
decision-making through multiple decision trees.
Logistic Regression: A simple yet effective statistical model for binary classification, requiring minimal
computational resources.
Decision Tree Classifier: A model that makes hierarchical decisions based on feature thresholds,
enabling fast inference.
Neural Network (MLP Classifier): A deep learning-based model with hidden layers to capture complex
relationships while maintaining reasonable throughput performance.
XGBoost Classifier: A gradient-boosting algorithm optimized for speed and accuracy, making it
well-suited for edge computing applications.
The following architecture will process machine learning data within a private network, as represented
in Figures 12a and 12b.
Figure 12a: Working Principle of ANN/ML Model within Private 5G
Figure 12a represents the working principle of the ML/ANN model within private 5G in smart mining.
Each model is trained and tested by using some sample of data split to ensure generalization and prevent
overfitting to achieve the desired throughput.
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Cloud-based processing involves an inherent delay due to data transmission. We can calculate network
delays by considering some n-MB data packet size per EU data transmission. The transmission delay can be
computed as: Latency = Data Size (MB)/Bandwidth (Mbps)
Figure 12b: AI-Based Feedback Control System for Edge Processing
The efficiency of data-driven smart mining is directly linked to how quickly and effectively sensor data is
analyzed and acted upon. Private 5G networks enable continuous, high-speed data transmission, which
is crucial for real-time AI-based decision-making rather than sending data to a centralized cloud server.
The edge computing infrastructure processes information locally, drastically reducing latency and
network congestion.
Limitations of Traditional Connectivity Solutions:
Public 4G/5G Networks
While urban areas benefit from dense network coverage, rural regions often experience weak signal
reception, network congestion, and slow data transmission speeds. Additionally, public networks are
optimized for consumer use, making them unsuitable for large-scale, industrial IoT applications like the
precision grading of coal.
Satellite-Based Internet (Starlink, VSAT)
While satellite internet provides connectivity in remote locations, high latency, limited bandwidth, and
costly deployment make it impractical for real-time IoT applications requiring instantaneous AI-based
decision-making.
Wi-Fi and Mesh Networks
Traditional Wi-Fi based mining area networks require extensive infrastructure deployment, repeater
installations, and constant maintenance, making it difficult to scale across large mining lands, mountainous
terrains, or dense vegetation areas.
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To demonstrate the practical implications of these restrictions, we examined three real-world connectivity
scenarios depicted in Figure 13 within the campus of NIST University showing the Reference Signal Received
Power (RSRP) ratio of public 4G (blue) public 5G (green) and private 5G (red). Private 5G shows huge
improvements with signal power in the -80dBm range at the same location as compared to other two.
RSRP
Measures the power of the signal
received from the public cell tower of
mobile network operator (MNO)
Optimal Range:
• Excellent: ≥ −89 dBm
• Good: −90 to −104 dBm
• Fair: −105 to −114 dBm
• Poor: −115 to −124 dB
• Very Poor: ≤ −125 dBm
Figure 13: CDF of RSRP for Public 4G, Public 5G, and Private 5G Networks
Benefits/Expected Outcome
There are several important advantages delivered by private 5G technology in mining operations.
Safety Improvement
Autonomous vehicles and remote monitoring systems that operate in private 5G can reduce the likelihood of
people being exposed to dangerous situations. The safety of employees will increase significantly by potentially
reducing the impact of hazardous environments.
Increased Operating Efficiency
Mining companies can use automation and real-time data analysis to increase productivity, reduce costs,
and maximize resources. With high-speed data transfer and applied AI models, mining operations experience
improved monitoring, real-time decision-making, and predictive maintenance capabilities.
Environmental Sustainability
Improved resource management, reduced waste and more efficient energy use reduces the environmental
impact of mineral extraction.
Cost Reduction
Predictive maintenance reduces the downtime of equipment and maintenance costs, while automation and
remote operation reduce the need for human labour under dangerous conditions. In just the one example
provided, IoT sensors can be used to streamline coal grading for significant cost reduction through
process improvement.
Improvement of Efficiency
Solutions based on data simplify the task, reduce waste, and increase overall efficiency. The return on
investment (ROI) is seen in the form of reduced operating costs and downtime. With the application of ML/AI
models, efficiency and performance continue to increase, having a real impact on the mining industry over time.
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Figure 14: Operational Transformation with Private 5G
Conclusion
The advent of private 5G in mining is not merely an upgrade—it is a revolution. By unlocking unprecedented
operational efficiency, enabling real-time decision-making through IoT and AI, and fostering safer,
zero-harm workplaces, private 5G positions the industry at the forefront of the Fourth Industrial Revolution.
As global demand for sustainable resource extraction intensifies, embracing this technology moves from
a strategic advantage to existential imperative. The mines of tomorrow will thrive not by the depth of their
reserves alone, but by their ability to harness private 5G’s transformative power—bridging productivity,
planetary stewardship, and human progress.
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Acknowledgements
Dr. Sukant K. Mohapatra for his continuous encouragement and support
Dr. Vanlin Robinson for his effortless help and guidance
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