Managing critical infrastructure and large industrial facilities has always demanded high precision and operational agility. As systems grow in complexity and the requirements for uninterrupted operation become more stringent, traditional monitoring methods are no longer sufficient. Integrating data from drones and other IoT devices into the operational management loop is becoming essential. This shift enables new approaches to operation, maintenance, and safety, ensuring a higher level of control and transparency.
Challenges in Industrial Operations Management
Industrial operations management faces a range of practical challenges. These include the need to monitor vast areas (e.g., power lines, oil pipelines, railway tracks), oversee equipment in hard-to-reach or hazardous zones, and the requirement for rapid incident response. Manual inspections and visual surveys are labor-intensive, costly, and often not effective enough. Furthermore, the volume of data that needs to be collected, analyzed, and used for decision-making is increasing, necessitating new approaches to its integration and processing.
Industrial systems generate enormous amounts of information, but this data often remains fragmented, stored in isolated systems, and not fully utilized for operational management. The task is to consolidate these data streams—from equipment sensors to drone footage—into a unified information space where they can be processed and transformed into actionable insights.
Drones and IoT as Data Sources for Operational Loops
Drones and IoT devices are powerful tools for real-time data collection. Drones equipped with high-resolution cameras, thermal imagers, multispectral, and LiDAR sensors can quickly and safely survey large areas, inspect high structures, and detect defects, leaks, temperature changes, or other anomalies. They provide visual and geospatial data that was previously inaccessible or required significant resources to obtain.
IoT devices, such as wireless sensors installed on industrial equipment, bridges, pipelines, or other infrastructure components, collect data on vibration, temperature, pressure, humidity, noise levels, and other parameters. This data, combined with information from drones, creates a comprehensive picture of the asset’s condition. Integrating this diverse data into a unified operational loop allows for:
- Proactive Maintenance: Identifying potential issues before they arise, enabling planned repairs and minimizing downtime.
- Resource Optimization: More efficient use of personnel and equipment through accurate data on maintenance needs.
- Enhanced Safety: Reducing risks to personnel by using drones for inspections in hazardous areas.
- Rapid Response: Timely information on incidents and the ability to make quick decisions.
Technological Prerequisites: 5G, Expanded Internet Access, and Standardization
The integration of drones and IoT into operational management has been enabled by several technological trends:
- Development of 5G Networks: The growing number of 5G subscriptions and the deployment of 5G Standalone are creating the foundation for more reliable and faster connectivity for IoT devices, including drones. According to the Ericsson Mobility Report of November 2025, 5G will become the dominant mobile access technology by subscription count by the end of 2027. This is crucial for transmitting large volumes of data (video, 3D models) from drones in real-time and for ensuring the low latency required for remote control.
- Expanded Internet Access: Despite the existence of offline population segments, the global expansion of internet access is driving the adoption of digital solutions across various industries. According to ITU Facts and Figures 2025, approximately two-thirds of the world’s population were internet users as of last year. This creates a favorable environment for cloud-based IoT platforms and remote management.
- Protocol Standardization: Unified protocols are essential for the effective integration of data from diverse devices. OPC UA serves as a crucial normalization layer for machine data before its transmission to SCADA/MES/edge analytics for Edge AI in OT. This allows different systems to exchange data, simplifying their integration and analysis.
Cybersecurity and AI Risk Management in the Context of IoT Data
The increasing number of connected devices and data volumes introduce new cybersecurity challenges. Industrial systems and critical infrastructure are attractive targets for cyberattacks, making the implementation of drones and IoT require special attention to data and system protection. Cybersecurity standards for industrial systems, such as ISA/IEC 62443, are a series of standards for the cybersecurity of industrial automation and control systems. For critical infrastructure, CISA CPG covers recommended practices for IT and operational technology owners. These standards provide frameworks for developing and implementing secure IoT solutions.
Furthermore, the advancement of AI and the need for its risk management (NIST AI RMF) are becoming important for analyzing data collected by drones and other IoT devices. AI models that process this data to detect anomalies or make predictions must be reliable, transparent, and resistant to manipulation. AI risk management includes assessing potential biases, ensuring data privacy, and developing mechanisms for verifying and validating AI outputs.
Common Mistake: Starting with Platform Selection, Not Data Flows
One of the most common mistakes in implementing remote monitoring systems is that clients begin by selecting a specific IoT platform or vendor solution without first analyzing their data flows and operational needs. This approach often results in the chosen platform not meeting actual requirements, being unable to effectively integrate with existing OT systems, or requiring significant rework, thereby increasing costs and extending implementation timelines.
Instead, it is essential to first clearly define what data is needed, where it will originate (drones, sensors, existing SCADA/MES), how it will be processed, who will use it, and for what purposes. Only after a thorough understanding of data flows and operational scenarios should one proceed to select an architecture and appropriate technological solutions that can ensure the required functionality, scalability, and security.
Operational Scenario: Integrating OT and IT Data for Production Optimization
Consider a typical scenario of integrating OT and IT data for production optimization at a large industrial enterprise. Drones regularly conduct inspections of equipment and infrastructure, collecting visual data, thermal images, and 3D models. Simultaneously, IoT sensors on conveyor belts, engines, and other critical components collect data on vibration, temperature, and energy consumption.
This data from drones and IoT sensors is transmitted to Edge devices, where initial processing and normalization occur, for example, using the OPC UA protocol. Subsequently, the aggregated and normalized data is sent to a centralized IoT platform, such as AZIOT (an IoT platform for physical environment management). This platform integrates with existing OT systems (SCADA, MES) to obtain contextual data about production processes and with IT systems (ERP, asset management systems) for maintenance and resource planning.
On the AZIOT platform, data is analyzed using AI models to detect anomalies, predict equipment failures, and optimize production parameters. For instance, AI can identify increased engine vibration, correlating it with data on its age and maintenance history from the ERP, as well as thermal images from a drone indicating overheating. Operators receive real-time alerts, allowing them to proactively schedule maintenance, minimize the risk of unexpected downtime, and optimize the production process.
Such integration not only enhances efficiency and safety but also creates a unified digital model of the enterprise, where all data is accessible for analysis and informed decision-making.
IoT Data Integration Readiness Checklist
- Operational Goals: Business objectives and KPIs for remote monitoring have been defined (e.g., reduced downtime, decreased operational costs, increased equipment availability).
- OT System Audit: An audit of SCADA, PLC, MES systems and their data transmission capabilities has been conducted (supported protocols, API availability, update frequency).
- Data Flow Map: A diagram of data movement from sensors/drones to final analytics and decision-making systems has been created, specifying formats and protocols.
- Network Infrastructure: The readiness of 5G, Wi-Fi, or wired networks to ensure stable connectivity with IoT devices across all site areas has been assessed.
- Cybersecurity Policy: A policy for IoT devices and data has been developed, considering ISA/IEC 62443 and CISA CPG standards, including network segmentation and access management.
- AI Risk Management: Approaches to managing AI model risks (per NIST AI RMF) for data analysis have been defined, including model validation and monitoring.
- IT Integration Plan: A plan for integrating OT data with ERP, EAM, and analytics platforms has been formed, with responsible departments identified.