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Industry 4.0/IIoT data networks are complex ecosystems that integrate sensors, communication protocols, data processing infrastructure and security measures. Their primary function is to enable the seamless flow of data – allowing real-time monitoring, control and optimisation of manufacturing processes in a smart factory.

A data network forms the backbone of the smart factory. How well you design and implement the network will determine how likely you are to achieve the full benefits of Industry 4.0 technologies. The robustness of your network is critical – so your electronics must be protected by the right enclosures.


  • Sensors and actuators are responsible for collecting data from the physical world. They monitor temperature, pressure, humidity, vibration and more. Actuators perform actions based on commands, such as turning a valve or adjusting a motor’s speed. Sensors provide the initial data inputs, while actuators can execute commands based on data analysis, allowing for real-time adjustments in the manufacturing process. Enclosures for sensors must be small and tough because they are likely to be close to the action.
  • Edge Devices/Gateways – these act as intermediaries between sensors/actuators and the central data processing unit. They collect, preprocess and filter data locally – reducing latency (and bandwidth requirements). They also serve as a point of security, allowing selective data transmission to the cloud or central servers. Enclosures for these devices must be resilient and reliable: losing a sensor is bad enough, but losing a data node is even more serious.
  • Communication protocols are the rules and conventions that facilitate communication between devices and systems. Protocols such as MQTT, OPC UA, and HTTP are commonly used in IIoT to ensure data integrity, security and efficiency during data exchange.
  • Local Area Network (LAN) – this connects edge devices, controllers and machines within a localised area of the factory. The LAN facilitates data exchange between devices, enabling control and monitoring using technologies such as Ethernet and Wi-Fi.
  • Cloud/server infrastructure for centralised data storage, processing and analysis. This part of the system stores historical data, runs analytics, and allows remote monitoring and control of the factory. Cloud platforms such as AWS, Azure or private servers can be employed.
  • Data storage systems hold historical and real-time data collected from the factory floor. Databases such as SQL or NoSQL store and manage data efficiently for reporting, analytics and machine learning applications.
  • Security measures are crucial to protect sensitive data and the integrity of the manufacturing process. Elements such as firewalls, encryption, authentication mechanisms and intrusion detection systems safeguard the network against cyber threats.
  • Visualisation and Human-Machine Interface (HMI) tools provide a user-friendly way to monitor and control the factory. Graphical displays, dashboards and alerts empower operators and engineers to manage processes more efficiently. HMI enclosures must be ergonomic – making displays easy to view and controls comfortable to operate.
  • Analytics and machine learning modules analyse data to gain insights, predict maintenance needs, optimise processes and detect anomalies. Advanced analytics and machine learning algorithms help the system and its operators to make data-driven decisions – improving overall production efficiency.
  • Redundancy and failover systems provide backup solutions to ensure continuous operation in case of network or hardware failures. They minimise downtime and maintain data integrity by switching between components and networks seamlessly.
  • Quality of Service (QoS) and traffic prioritisation mechanisms ensure the network conveys critical data swiftly and efficiently. QoS settings help to guarantee that mission-critical data – such as safety-related information – is transmitted with minimal latency and without interference.