by Tony Paine, Platform President, Kepware
IIoT promises to let everything within an industrial environment connect to get complete visibility into operations and allow the best real-time decisions—with or without human intervention.
In a perfect world, the IIoT connects all hardware and software components (the Things) that comprise an automation system. These connections will bring benefits that include enabling Things to share information, learn about their surroundings, and auto-tune themselves for optimum throughput and minimal downtime. Operational personnel will be able to remotely assess and manipulate all aspects of the production line without the need for dedicated on-site expertise.
But these benefits are contingent upon resolving key challenges—several of which the industry has been working on for years.
An industrial automation process includes mechanical, digital and human components. At any time, one of those parts may have information that is valuable to others. Much of that information has existed within industrial environments and has been shared for some time now—just at a much smaller scale than as outlined in IIoT scenarios and under different names (SCADA, M2M, Predictive Maintenance and Process Optimization).
Today, there are several changes impacting the scale and speed of the IIoT:
• New vendors are entering the market, trying to consolidate data into actionable information, unify historical solutions, and bridge the gap between the public and private operational domains.
• Our society increasingly relies on the Internet and has more connected tools available than ever before.
• Technology is no longer cost-prohibitive: We can network-enable anything with low-cost sensor technology, unlocking and storing data that was previously unavailable.
• Finally, the next generation of engineers has grown up with technology that is rich, easy to use, and everywhere—creating an expectation that existing control systems will be comprised of technology that plugs in and works with little effort.
At the enterprise level, multi-site awareness will provide critical insight for competitive strategic planning, as well as the opportunity to integrate beyond organizational boundaries for the purpose of leveraging a third party’s business services.
As industry builds out the IIoT, its biggest challenge will be in seamlessly Internet-enabling the Things that live at the edge of the network. Industry-wide, this edge contains trillions of Things that contain data points that may need to be analyzed and converted into information. Unfortunately, the edge of the network is also the furthest removed from the information technology (IT) we have become accustomed to using when Internet connectivity is required.
Identifying Things within the Internet
For Things to communicate with each other, they must be uniquely identifiable on the Internet. Historically, this has taken place through the assignment of an Internet Protocol (IP) address. As industry looks ahead to the trillions of Things to be connected, focus has been on adopting the IPv6 standard, which defines a 128-bit address capable of uniquely identifying 340 undecillion (340 × 1036) addressable items (compared with only 4 billion addressable items using today’s IPv4 standard). Though this range will more than cover the requirements of IIoT, it will be difficult—if not impossible—to manage this number of addresses effectively on a global Internet scale. Typically managed by Naming and Number Authorities with the aid of network administrators, this will be an impediment as Things are added at an unprecedented rate.
Discovering Things and the data they possess
Once a Thing can be identified, the next challenge is how to let other interested parties discover it exists and what data it holds. Of course, a Thing should be able to restrict discovery of all or some of its data based on security requirements. Balancing ease-of-discovery with the rigid constraints of security will be fundamental to the success of IIoT and must be possible by personnel without PhD degrees in cybersecurity.
Managing massive amounts of data
The trillions of Things will produce much more than trillions of data points (industry currently measures the number of data points in zettabytes or 1021 bytes), all of which will need to be collected, analyzed and possibly archived. Moving this much data over the Internet will consume much larger levels of bandwidth, which could result in the degradation of service as well as higher costs for Internet carriers, service providers and ultimately end users. Moreover, archiving this data for future analysis will require massive amounts of data storage and a new generation of scalable applications for honing in on points of interest in a timely manner.
Navigating connectivity outages
The Things that make up the IIoT, as well as the communication media that link them together, will not be available 100% of the time. While some downtime may be scheduled, there will be physical or environmental changes that result in intermittent or longer outages. But in some cases, data loss is unacceptable or operators need to know the criticality of variances in the data in real time.
Integrating existing infrastructure into new IIoT strategies
Industrial Things have made data accessible over private networks for decades through both open and proprietary protocols. But these networks have ignored complexities like security in the interest of optimizing network operations and third-party integration. The typical lifecycle for industrial Things exceeds 20 years, so manufacturers have expectations of integrating their existing infrastructure into new IIoT strategies. Detailed security assessments will be necessary before manufacturers feel confident hooking the Internet into existing private networks and the data they contain.
Leveraging the power of cloud computing
To alleviate the preceding challenges, IIoT strategies will push data into a centralized cloud platform. Cloud computing and its multitudinous resources can handle the zettabytes of data that will be collected, analyzed and archived. Furthermore, the overall uptime of cloud platforms continues to trend higher as they become more resilient.
Communicating with devices on the edge
The actual source of data pushed into the cloud comes from Things that live at the edge of the network. The edge bridges the gap between IT and operational technology (OT, simply, people and technology that support industrial processes), where the resources available in the cloud are not directly available. OT encompasses industrial networks that have their own nuances and introduce additional challenges.
Connecting disparate communications mediums
Often, industrial networking technologies do not use Ethernet as their physical communications layer. Depending on the environment and the Things that comprise a system, industrial networks can use anything from RS232/485 to modems to proprietary wiring. Likewise, the data protocols over these communication mediums are not likely to be IP derivatives. The result is a hodgepodge of industrial networks created with no thoughts of the possibility of being connected to the Internet.
Using nonstandard methods of identification
Unlike IP addresses in the IT world, many industrial Things have their own addressing schemes for uniquely identifying themselves on the network. These schemes vary by vendor and type and may or may not have built-in discovery mechanisms. It takes innate knowledge of an integration expert to interconnect the Things in a way that makes them function as a whole.
Determining a request/response model
Industrial Things have historically followed a request/response model. If a particular Thing is interested in a piece of data sitting in another Thing, it will make an appropriate connection, request the data, and wait for a response containing the result. Although this pull model is fine for Things living within the same digital boundary of OT, security and scalability requirements render this model unacceptable for the outside IT world trying to look in. Instead, IIoT prefers a push model, where industrial data flows out to a cloud platform.
Enabling short-term data storage
Within the context of a single industrial network, we may find thousands of Things that, together, could generate several thousand data points. Though this sounds like a small set of data, the real-time requirements of OT will demand these points be sampled at sub-millisecond rates for data change detection. In the past, this high-frequency data would be simply analyzed, acted on accordingly and thrown away. As we move to making this data available to IIoT, we will require short-term storage to ensure it can be pushed to other parties when they are available.
IIoT edge solution
It will take a new communications platform to seamlessly integrate industrial data into the IIoT. This platform must allow for the intricate realm of OT and the state-of-the-art and rapidly changing domain of IT. Within OT, the platform must understand the various network topologies and data protocols. It must be able to automatically discover and identify industrial Things and the data they contain, as well as handle the storage of high-frequency updates. Within IT, the platform must be able to transform the data it collects and push it into the cloud through IIoT standards.
Emerging standards include:
• Asynchronous Messaging Queuing Protocol (AMQP)
• Message Queueing Telemetry Transport (MQTT)
• Constrained Application Protocol (COAP)
• Data Description Services (DDS)
These standards allow for the retransmission of data if it does not reach its destination. With the lack of computer networking infrastructure in OT, this platform must be embeddable and run within a stand-alone appliance or an edge-based switch or router where IT and OT converge.
Its flexibility will enable industrial data to be sampled cyclically or based on some event or condition and be published to the cloud independently of data collection. Data filtering should be available through basic analytics. Last, user setup should be minimalized by automating as much configuration as possible.
As industry continues to define IIoT, the concepts and realization of the optimal embedded IIoT solution will continue to evolve.