Skkynet | Data Anywhere

“I t all sounds fine on paper, but will it work for me?” That’s a question that engineers and system integrators often ask when the topic of Industrial IoT comes up. There are so many ways it has to fit. Industrial systems are like snowflakes–every one is unique. Each facility, factory, pipeline, or power plant was built for a particular purpose, in a different part of the world, at a specific time in plant automation history, when technology had advanced to a certain level. We see a wide range of machines, tools, sensors, and other equipment used with endless combinations of proprietary and home-grown software and data protocols. Over time, plant modifications and expansions along with hardware and software upgrades bring still more variety.

If this diversity isn’t challenge enough, new questions are now popping up about the Industrial IoT itself: How to get started? What service provider to use? What approach or platform is best to take? What are the cost benefits?

Putting all this together, it becomes clear that a good Industrial IoT solution should be a comfortable fit. It should connect to virtually any in-plant system with a minimum of fuss, and provide links to remote systems as well. It should be compatible with multiple data protocols and legacy systems, and yet also integrate seamlessly with future hardware and software. Like putting on a new suit, the ideal is to ease into the IoT without disrupting anything.

Working towards that goal, here’s what a good system should do:

Support diverse data communication protocols: OPC, both OPC “Classic” and UA, plays an important role in simplifying and unifying industrial data communications. Any Industrial IoT platform should support OPC, along with common industrial fieldbuses like Modbus, Profibus, HART, DeviceNet, and so on. It should also support more specialized standards like IEC 61850, CAN, ZigBee, and BACnet. In addition to these, Industrial IoT should be compatible with non-industrial standards like HTML and XML for web connectivity, ODBC for database connectivity, DDE for connecting to Excel if needed, as well as the ability to connect to custom programs.
Connect to embedded devices: The “of Things” part of the Internet of Things refers primarily to embedded devices. Sensors, actuators, and other devices are getting smaller, cheaper, and more versatile every day. They should be able to connect–either directly or via a wired or cellular gateway–to the cloud. This is an area where SCADA can provide a wealth of experience to the Industrial IoT, and in turn benefit significantly from the expanded reach that Internet connectivity can provide.
Work with new or legacy equipment and facilities: Since the introduction of the DCS and PLC in the 1970’s, digital automation has been growing and evolving. While new technologies are constantly being adopted or adapted, many older systems continue to run. With so much engineering, effort, and capital invested in each project, plant management is often reluctant to make changes to a working system. To be accepted in the “If it ain’t broke, don’t fix it” world, an Industrial IoT system should be able to connect to, but not intrude upon, legacy systems. Of course, for new systems, it should do likewise.
Use existing tools, or better: The Industrial IoT doesn’t need to reinvent the wheel. Most industrial automation systems have a solid, working set of tools, which might include DCS and SCADA systems; HMIs; MES, ERP and other kinds of databases; data historians, and more. A compatible Industrial IoT implementation should work as seamlessly as possible with all of these tools, using the appropriate protocols. At the same time, it would do well to offer connections to improved tools, if required or desired.
Meet Big Data requirements: Among the new tools, the ability to connect existing or future industrial systems with Big Data is one of the main attractions of the Industrial IoT. A compatible Industrial IoT solution should provide connectivity and the performance necessary to feed whatever Big Data engine may be chosen.
Allow for gradual implementation: Automation experts and proponents of the Industrial IoT are quick to point out that there is no need to implement this all at once. They often recommend a gradual, step-by-step implementation process. Start with a small data set, an isolated process or system, and build from there. Bring in users as needed. Once you are comfortable with the tools and techniques, you can build out. Naturally, you’ll need an IoT platform that supports this approach.

How Skkynet Fits

With Skkynet, compatibility for the Industrial IoT comes in three components that work seamlessly together: DataHub^®, Embedded Toolkit (ETK), and SkkyHub™.

The Cogent DataHub^® connects directly to in-plant systems via OPC, Modbus, ODBC and DDE, and is fully integrated with the Red Lion Data Station Plus, to connect to 300 additional industrial protocols. The DataHub supports data aggregation, server-to-server bridging, database logging, redundancy, and other data integration functionality. It also offers WebView, a flexible, web-based HMI.

The Embedded Toolkit (ETK) is a C library that provides the building blocks for embedded systems to connect and communicate with SkkyHub or the DataHub. It has been compiled to run on gateways from Red Lion, B+B SmartWorx, NetComm, and SysLINK, as well as devices from Renesas, Lantronix, Raspberry Pi, Arduino, ARM, and more.

These two components can be connected to and integrated with virtually any industrial system. They can be used separately or together, and can serve as the first stage of evolution towards the cloud at any time, by connecting to SkkyHub.

The SkkyHub™ service collects and distributes real-time data over networks, both locally and remotely. Connecting to the DataHub or any ETK-enabled device, SkkyHub provides secure networking of Industrial IoT data between remote locations, and remote monitoring and supervisory control through WebView.

Skkynet’s Industrial IoT software and services are in wide use today. You can find them connecting manufacturing facilities, wind and solar farms, offshore platforms, mines, pipelines, production lines, gauges, pumps, valves, actuators, and sensors. Their unique combination of security, speed, and compatibility with virtually any industrial system makes the DataHub, ETK, and SkkyHub well-fitting components of the Industrial IoT.

T he Industrial IoT is different from the regular IoT. Mission-critical industrial systems are not like consumer or business IT applications. Performance is crucial. Most IT systems are built around a relational database, a repository of data that clients can add to or access, where a response time of a second or two is acceptable. IT data is typically sent across a network via HTML or XML, which adds complexity to the raw data, and consumes bandwidth. Although fine for office or home use, these technologies are not sufficient for the Industrial IoT.

In a typical industrial system, the data flows in real time. It moves from a sensor, device, or process through the system, often combining with other data along the way, and may end up in an operator’s control panel, another machine or device, or special-purpose data historian. As plant or field conditions change, the data arrives in real time, and the system or operator must react. A robotic arm or other device can send hundreds of data changes per second. Tiny, millisecond fluctuations in the data set can have significant effects or trigger alarms, and often each minute detail needs to be accessed in a trend chart or historical database.

Achieving this kind of performance on the Industrial IoT demands an exceptional approach to data communication.

A real-time, in-memory database keeps the data moving. The data needs to flow quickly and effortlessly through the system, and an in-memory database is needed to support these rapid value changes. A relational database, the familiar workhorse of the IT world, is not built for this specialized task. It takes too long to write records, process queries, and retrieve information. Thus, an in-memory, flat-file database, is a good choice, allowing for higher data throughput.
High-speed data integration connects any data source with any user. A key task of the in-memory database is to integrate all sources of incoming data. If all communication is data-centric (see below), then every data source can be pooled together into a single, universal data set. This design keeps the data handling as simple as possible, allowing any authorized user to connect to any specified combination of data inputs in real time.
Publish/subscribe beats polling. In a publish/subscribe, event-driven model, a user makes a one-time request to connect to a data source, then gets updates whenever they occur. By contrast, polling sends regular, timed requests for data. This wastes resources when data changes are infrequent, because multiple requests might return with the same value. At the same time, polling is also inaccurate during rapid change, because a burst of several value changes may occur between polling cycles, and will be completely lost.
High-speed “push” data sources are most effective. The data should be pushed out to the system, and then pushed to the user. In addition to being a better security model, this approach is also more efficient. To “pull” data from a source requires polling, which takes longer and uses too much bandwidth, because each data update requires two messages: a request and a reply. Push technology only requires one message, which is more efficient, consumes less bandwidth, and also enables machine-to-machine communication.
Data-centric, not web-centric, design gives the best performance on the cloud. Transcoding data at the source takes time, and requires resources on the device which many smaller sensors may not have. By keeping the data in its simplest format, with no HTML or XML code, the lowest possible latency can be achieved. The raw data flows from the source, through the cloud, to the user as quickly as possible. When it arrives it can be converted to other formats, such as HTML, XML, SQL, etc. Different users, such as web browsers, databases, spreadsheets, and machine-to-machine systems can access a single data source at the point of its arrival, reducing the volume of data flow in the system.

Skkynet’s implementation

Following these principles, Skkynet’s SkkyHub™ and DataHub^® provide in-plant or IoT networking speeds of just a few milliseconds over network latency, with a throughput of up to 50,000+ data changes per second. Their high level of performance is achieved by combining real-time, in-memory database technology with publish/subscribe, pushed data collection and a data-centric approach to communication.

The “Hub” technology in DataHub and SkkyHub is a real-time, in-memory, flat-file database, used in hundreds of mission-critical systems worldwide for over 15 years. Designed from the ground up for industrial data communications, the DataHub and ETK work by converting all incoming data into a simple, internal, raw-data format. This raw data can be integrated and transmitted at very high speeds.

At the plant level, the DataHub collects, integrates and redistributes process data in real time. Selected sets of data can be passed seamlessly to the IoT simply by connecting the DataHub or ETK to SkkyHub. At the cloud level, SkkyHub provides the same real-time data collection, integration, and distribution. IoT performance now approaches the actual network propagation speeds of the Internet, with virtually no added latency.

Quite honestly, we shouldn’t expect the typical IoT platform to provide this level of performance. Few, if any, were designed for the Industrial IoT. It should come as no surprise that a concept as disruptive as “Industrial Internet of Things” may require new approaches for proper implementation. And in addition to performance, industrial applications have unique security and compatibility requirements. When choosing a solid, robust platform for Industrial IoT, these are all critical factors to consider.

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