Fog computing for industrial automation

How to develop a secure, distributed automation architecture in a data-driven world: Two examples and five advantages of fog computing are highlighted.

03/08/2018


Figure 1: The illustration shows the eight pillars of the architecture the OpenFog Consortium uses, covering security, scalability, openness, autonomy, reliability, agility, hierarchical organization, and programmability. Courtesy: OpenFog Consortium The manufacturing industry is experiencing substantial benefits as industrial operators use the Industrial Internet of Things (IIoT) to automate systems, deploy sensors to measure, monitor, and analyze data, improve efficiencies, and increase revenue opportunities for manufacturing operations. Using eight pillars of a fog computing architecture can help.

The amount of data from these newly-connected plants can be measured in the petabytes (1 million gigabytes): Millions of streaming, connected sensors on industrial control systems (ICSs), dozens of autonomous drones, industrial robots, video surveillance cameras covering plants, and so on.

Traditional information technology (IT) approaches to operational technology (OT) environments cannot keep up with the necessary volume, latency, mobility, reliability, security, privacy, and network bandwidth challenges in controlled, supplier-connected, or rugged operational environments. It's time for a new architectural approach to allow IIoT to reach its potential with fog computing. 

Defining fog computing

Fog computing is designed for data-dense, high-performance computing, high-stakes environments. Fog is an emerging, distributed architecture that bridges the continuum between cloud and connected devices that doesn't require persistent cloud connectivity in the field and factory. Fog works by selectively moving compute, storage, communication, control, and decision making closer to IoT sensors and actuators, where the data is generated and used. It augments, not replaces, investments in the cloud to enable an efficient, cost-effective, secure, and constructive use of the IIoT in manufacturing environments.

Fog is sometimes referred to as edge computing, but there are key differences. Fog is a superset of edge functionality. The fog architecture pools the resources and data sources between devices residing at the edge in north-south (cloud-to-sensor), east-west (function-to-function or peer-to-peer) hierarchies working with the cloud for maximum efficiency. Edge computing tends to be limited to a small number of north-south layers often associated with simple protocol gateway functions.

Fog nodes are foundational elements of the fog architecture. A fog node is any device that provides computational, networking, storage, and acceleration elements of the fog architecture. Examples include industrial controllers, switches, routers, embedded servers, sophisticated gateways, programmable logic controllers (PLCs), and intelligent IoT endpoints such as video surveillance cameras. 

Fog architecture benefits for a factory

Factories can become more connected and can take advantage of streaming data through a layer of fog nodes. A fog node at the lower level of the hierarchy—for example, located on an individual machine—can be connected to a set of local sensors and actuators so it can analyze the data, interpret an anomaly, and then if authorized, could autonomously react and compensate for the problem or fix the issue. Alternatively, the fog node can send the appropriate requests for service higher up the fog hierarchy to more skilled technical resources, machine learning capabilities, or a maintenance service provider.

If the situation requires real-time decision-making—for example, shutting down equipment before damage occurs, or adjusting critical process parameters—fog nodes can provide millisecond-level latency analysis and action. The manufacturer doesn't have to route this real-time decision making through the cloud data center. This helps avoid potential latency issues, queue delays, or network/server downtime that could result in industrial accidents, reduced production efficiency, or poor product quality.

In the factory, fog nodes higher up in the hierarchy can have a broader perspective on industrial processes. They can add more functionality such as visualization of production line operation, monitoring the status of malfunctioning machines, tuning of production parameters, modification of production planning, ordering supplies, and sending alerts to the appropriate people. 

Fog architecture for an oil pipeline

To illustrate how fog computing works in more rugged environments, consider an oil pipeline with pressure and flow sensors and control valves and pumps. Traditionally, remote sensor readings are transmitted to the cloud using expensive satellite links for data analysis to detect abnormal conditions. The cloud would send commands back to the operator to adjust valve positions and so on.

This approach is suboptimal because network bandwidth is very expensive and connectivity can go down—especially in severe weather. Transmitted data may also be susceptible to hackers and the round-trip latency is too long (hundreds of milliseconds), which causes slower reaction time to react to critical incidents.

With the addition of fog computing, a hierarchy of local fog nodes is placed near the pipeline to connect to sensors and actuators with inexpensive and fast local networking facilities. The nodes add additional security, lessening the opportunity for breaches, and can be given the authority to react to abnormal conditions in milliseconds, quickly closing valves to greatly reduce the severity of spills. The fog nodes run over wireline, optical, and wireless networks and also inside these networks, making it ideally suited for connection to industrial elements based on wired supervisory control and data acquisition (SCADA) systems, OPC UA interfaces, Modbus, etc. Analytics on the fog node at the local site reduces demand of the bandwidth to the cloud, keeping overall costs low.

Balancing control between the cloud and the fog produces a better outcome across the business (costs, control, safety and security). Moving most of the decision-making functions to the fog—and using the cloud to occasionally report status or receive commands or updates—creates a superior control system.


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