Tag Archives: PLC

IoT versus SCADA/DCS Data Acquisition Patterns

Introduction

The term Internet of Things (IoT) is used in a variety of contexts where it is often misunderstood, because it can be replaced by other terms much better describing the matter we deal with or the definitions are not compliant with each other. Let me remind you about the very beginning of this term life.

That ‘Internet of Things’ Thing

The phrase “Internet of Things” started its life as the title of a presentation made in 1999 and aimed at explaining a new idea of radio frequency identification (RFID) in the context of the supply chain performance. It is clear that it doesn’t mean that someone has any right to control how others use the phrase, but my point is that a precise term definition is important for working together on: common rules, architecture, solutions, requirements, capabilities, limitations, etc. In practice having a common definition it is possible to check a selected technology, solution or product capabilities against requirements of the application entitled to use this term.

The main goal of this article is to contribute to the community work aimed to distinguish the IoT applications domain features. The main challenge faced up is to narrow the definition to make it unambiguous and meaningful.

In most publications I know the term IoT can be simply replaced with the following well-known terms:

  • SCADA – Supervisory Control and Data Acquisition
  • DCS – Distributed Control Systems

and the text still will be perfectly OK. In this context the “sensor” term plays the role of the “thing” and the nodes network is a synonym of the Internet. No one usually takes care whether we are talking about the temperature sensor in a bedroom or in a boiler drum in a power plant except that in case of a power plant sometimes the prefix Industrial is added. To make our life easier let’s forget about the “I” (Industrial) prefix at all because it doesn’t change the most important application domain features.

To illustrate the further discussion let me provide examples that can be recognized as SCADA/DCS and IoT applications respectively.

SCADA/DCS example

Let’s assume that an OPC UA Server exposes 123456 values representing the crude oil refining process. Using SCADA on top of this server we can monitor and manually control the process. Using DCS it is possible to implement a supervisory control algorithm to provide macro optimization.

For this scenario we can apply the following work-flow:

  • The server instantiates an OPC UA Information Model for the crude oil refining process.
  • All plant floor devices equipped with sensors are fetching data representing the current process state (for example the flow meter #A-4321 supporting the Modbus RTU communication protocol) and are waiting for data requests coming from the server communication engine.
  • The communication engine embedded in the server polls all plant floor devices including the flow meter #A-4321 to recover the current process state.
  • Finally, the OPC UA Server exposes the data (updates value attributes of the relevant variable nodes, e.g. #A-4321 object representing the virtual flow meter #A-4321) in his Address Space Management component (i.e. in the Address Space instantiated according to the Information Model of the crude oil refining process).
  • OPC UA Clients connected to this server are updated in a standardized way.

Note in this scenario that the OPC UA Client and OPC UA Server can establish connection over the Internet using any existing transport protocol, e.g. HTTP, HTTPS, TCP, UDP, AMQP. Selection of the transport protocol between the client and server is negotiated and limited by the OPC UA specification. To get more read OPC Unified Architecture – Main Technological Features.

You can use the server I have exposed many years ago to test this scenario. The instruction how to connect is included in the following document:

http://www.commsvr.com/Products/OPCUA/OPCUAServers/OPCUAADIServer.aspx

IoT example

Do you think a box (or even pack) of cigarettes could be the “thing”. It has a bar-code, so it is the source of data. Is it the sensor – NO because the bar-code reader (industrial scanner) is the sensor in this case. Can we recognize the bar-code reader as the “thing” – again the answer is NO if the goal is to provide a GLOBAL cigarettes tracking system. The same applies to drugs for example. Is it an IoT solution – my answer is YES, no doubts. Is it SCADA/DCS –the answer is NO, because the server (undelaying communication engine) cannot poll all possible places spread over the world where the box could appear. There are two reasons:

  • It is impractical or even impossible to manage such a huge set of addresses.
  • The server doesn’t know when to poll because the relevant data appear as an event instead of a process state value.

Assuming that the server is interested or even allowed to collect product data of one vendor only – not all codes fetched by any bar-code reader are relevant to the server.

Is the “thing” smart – I don’t think we can call the bar-code something smart. Is it controllable – NO.

The most interesting observation is that we can recognize this use case as an IoT application, but we have not mentioned OPC, AMQP, MQTT, SOA, Internet, WI-FI, wireless, Modbus, etc. at all, but only that we have important mobile data and the solution is globally scoped. It is good because we can check the available technology capabilities against this application requirements. As I have said selected communication technology is not the goal, but we must know how it scales to applications like this.

Now, let’s replace the word GLOBAL by LOCAL (for example cash desks farm in a shop) and the same application is no longer an IoT deployment, isn’t it? It is true even if the cash desks are interconnected using the IP protocol (e.g. Intranet)!

Sometimes it is stressed that any IoT application must guarantee a high level of robustness, but importance of the sensor and data robustness requirement is applicable to many kind of applications, e.g. controlling an airplane engine during flight. The same engine could be monitored and tracked after landing in any airport using the local WI-FI by uploading archival data to a central advanced analytic system (like the cigarettes box bar-code). Is it IoT? It isn’t during the flight, but the solution is life sensitive. After landing it is IoT, but the reliability of the data and data transfer is not so important, isn’t it?

IoT Paradigms

My proposal of the Internet of Things definition is as follows:

Internet of Things is all about:

  • mobile data fetching – how to gather the data from mobile devices (things);
  • mobile data subscription – how to transfer the data over the Internet to a place where it could be processed;
  • mobile data processing – how to integrate the data into a selected application to improve process behavioral performance.

Data fetching is related to a variety of last mile communication technologies, for example RFID, WI-FI, VHF, Bluetooth, etc. Subscription could be supported using messaging systems, e.g. AMQP, MQTT, etc. A good candidate for leveraging data consumption is for example OPC Unified Architecture.

Referring to previous examples, the data fetching process looks very similar in both cases – we have a data source and a sensor coupled together at some point in time responsible for sampling the data. Analyzing the examples from the application functionality point of view we cannot compare them because there are no requirements defined at all – only very general descriptions are provided. It looks like application capabilities are not relevant to the term definition. It is the reason why the terms SCADA, DCS and IoT are used interchangeably neglecting the fundamental differences between the following data acquisition patterns:

  • Data polling–continuous checking of the sensors to see what state they are in, usually in multipoint or multidrop communication (a communication engine with multiple devices attached that share the same line) by sending a message to each device, one at a time, asking each to respond and send new data.
  • Data subscription– senders of messages containing the process data fetched by the sensor, called publishers, do not prepare the messages to be sent directly to specific receivers, called subscribers, but instead they categorize published messages into topics without knowledge of which subscribers, if any, may receive the message. Similarly, subscribers express interest in one or more topics and only receive messages that are of interest, without knowledge of which publishers, if any, there are.

It is worth stressing that in both cases reusability of the fetched data is assured. In data polling scenario the server coupled with the communication engine may be connected to by many clients at the same time. In data subscription scenario the publisher is responsible for multicasting the data to all attached subscribers directly or indirectly using a broker.

To deploy the IoT scenario:

  • the mobile data must be sent over the Internet (or Intranet) using messages;
  • the payload of these messages is consumed asynchronously by a server (e.g. OPC UA Server) responsible for exposing it in an address space;
  • the applications (e.g. OPC UA client) process the exposed data to reach selected key performance indicators (KPI).

It is required that the messages payload is formatted in a standardized way to be factored on the fetching site and meaningfully consumed by the analytic application (e.g. OPC UA client).

Data Acquisition patterns

In the above discussion the application functionality has been excluded as a factor, which can be used to recognize IoT applications. Now let’s analyze the impact of the data acquisition pattern on the application behavioral model.

Using data polling we must deal with the synchronous data acquisition pattern. In this case the application must follow the interactive behavioral model, because it actively polls the data source for more information by pulling data from a sequence that represents the process state in time. Such behavior is represented by an iterator, which is used to iterate through a data stream. The application is active in the data retrieval process – it controls the pace of the retrieval by sending the request messages at its own convenience. This enumeration pattern is synchronous, which means that the application might be blocked while polling the data source. Such polling pattern is similar to visiting the books shop and checking out a book. After you are done with the book, you pay another visit to check out another one. If the book is not available you must wait, but you may read what you selected.

On the other hand, in the reactive behavioral model, the application is offered more information by subscribing to a data stream, and any update is handed to it from the source. The application is passive in the data retrieval process: apart from subscribing to the source data stream, it does not actively poll the source, but merely react to the data being pushed to it. In this case, the application will not be blocked by waiting for the source to update. This is the push pattern employed by IoT. It is similar to joining a books club in which you register your interest in a particular genre, and books that match your interest are automatically sent to you as they are published. You do not need to wait but you must read what you get. Employing a push pattern is helpful in many scenarios, especially if data is available asynchronously as events.

The push model implemented by IoT requires additional resources responsible for multicasting the pushed data to all subscribers. This functionality may be accomplished by a middleware fulfilling the broker role or supported by the network infrastructure, e.g. IP multicast.

The fundamental differences between the interactive and reactive behavioral model must obviously impact the final application functionality, for example:

  • process controllability;
  • data destination discoverability;
  • maintainability.

Using data polling the request message may also contain data to control the state of a selected actuator. In this case the response message usually contains positive or negative acknowledge that can be used by the application as a condition selecting further activity. For example in case of communication disruption the request message may be resent. In case of actuator failure an alarm may be raised. In case of the pushing data scenario it is hard to implement remote control functionality in a similar way because the communication path is like a one-way route.

 

In interactive behavioral model the communication engine must have all information including addressing in advance to properly prepare request messages. The messages must be self-contained to be used by the network routing mechanism. In case of reactive behavioral model the application doesn’t know the source of data in advance. Therefore the sensors responsibility is to format the message and push it to an appropriate distribution channel. In this case the messages are not self-contained, because the information carried by them is only indirectly used by the routing mechanism.

For the polling data scenario configuration modification may be required after replacing the sensor if the data sources are not isomorphic for the data acquisition process. On the other hand the pushing data scenario requires that any replacement or modification of the data source must not need any modification of the application configuration.

Conclusion

From the above discussion we can derive the following dependency models:

Interactive applications (e.g. SCADA, DCS):
  • The application engine depends on the synchronous data acquisition engine – data is observed as a stream of entities.
  • The acquisition engine depends on the process devices (sensors or actuators), which it must know in advance.
Reactive applications (e.g. IoT):
  • The application engine depends on the asynchronous data acquisition engine – data is observed as a stream of events.
  • The data source depends on the data distribution channel.
  • The asynchronous acquisition engine depends on the distribution channel.
  • The data source and acquisition engine are associated by the data distribution channel proprietary mechanism.

There is a well suited open source project to start prototyping with this scenario.

https://github.com/mpostol/OPC-UA-OOI

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OPC UA Makes Highly Distributed Network Control Systems Possible

Integration

Nowadays, to be on edge, modern manufacturing automation systems have to be involved. Usually they consist of numerous different IT systems located at business management/operation and process control levels. It is broad class of applications domain where business IT and control systems are converged to make a large whole with the aim to improve performance as the result of the macro optimization and synergy effect. This domain is called Industrial IT. Frequently the systems are distributed geographically in multi-division organizations.

To deploy the mentioned above convergence the systems have to be integrated – must interoperate with each other. From integration we should expect improved performance as a result of synergy and macro optimization effects.

After integration the systems should make up a consistent system, i.e. each subsystem (as a component) must communicate with each others. The final information architecture is strongly dependent on organization, culture, type of technology and target process. Communication is necessary for exchanging data for production state analysis, operation actions scheduling, supervisory control and task synchronization in the process as a large whole.

Vast majority of enterprises declare that difficulties with the integration of the existing systems are the most important obstacle to expand the process control and business management support. Other major integration problems are diversification of the existing systems, their quantity and non-unified data architecture.

Integration process results in Large Scale Distributed Network Control Systems (LSDNCS). Systems belonging to this class are usually created in a multi-step integration process. To succeed, the process has to be governed by a well-defined information and communication architecture.

Integration Models

System integration means necessity of the information exchange. To exchange information we need an association between components. Going further, to instantiate association, i.e. to make the component interoperable, we need at the same time a common:

  • information representation –  a language (data type),
  • underlying communication infrastructure – a transport (protocol + medium),

We must be aware that establishing an association we are actually building information architecture – system structure. It is worth stressing that selection of the architecture development has a great impact on the final robustness, maintainability, expandability, dependability, functionality, and last but not least implementation costs.

Generally we have three possibilities:

Peer to peer approach: Common integration practice is to achieve short-term ad-hoc objectives by manually creating/proprietary dedicated point-to-point links between the subsystems everywhere it is useful (see fig. 1). Using randomly this approach we can establish numerous independent links ((k+n)(n+k-1)/2 where k, n – number of business and process control components appropriately). The number rapidly grows, e.g. it is equal 1770 links for n=10 ; k=50, and finally we have to deal with rapidly growing complexity leading to the communication chaos, which is difficult to be maintained.

In this model the information and communication architectures are closely coupled. This approach is very popular, but adversely affects all of the solution features.

Peer to peer approach

Fig. 1

Totalitarian approach: One option to overcome the communication chaos problem is to use an “all in one” product dedicated to both functions: process control and business management (see presentation). Usually, it is provided as one complex, total system – let’s call it a supper-system. Most of the MES (Manufacturing Execution System) vendors offer theirs products as a panacea for all problems of the chaotic system integration.

Actually supper-system does not solve the problem, it only hides it under a not transparent cover and makes the solution very difficult to expand and vendor related forever.

In this model the system distribution is reduced, and as a consequence many associations can be instantiated on the same platform without necessity to communicate. This model reduces complexity by reducing communication needs.

If strictly observed it could be a dead end.

Process Observer approach: The Process Observer is a consistent, homogenous real-time representation of the process control layer. It is a kind of the virtual layer, which is a “big picture” of the underlying process layer composed of unit data randomly accessible by means of a unified and standardized interface (see presentation). It allows sharing data from plant floor devices by the process and business management systems, using international standards of data exchange. Process Observer is like a bridge connection between the plant-floor control and the process and business management levels.

Thereby, the structure of the links becomes systematic and the existing functionality of the upper layers is preserved. Now, they can gather the process data in a unified, standardized way (see fig.2).

Using the Process Observer archetype the number of links between components can be substantially reduced and, what is very important is a linear function of the number of nodes.

Process Observer model greatly reduces the whole complexity and decrease dependency by decoupling application associations and underlying communication routes. Additionally, it allows applying systematic design methodology and building information architecture independently of underlying communication infrastructure.

Fig. 2

Fig. 2

Related articles

Real-Time Communication for Large Scale Distributed Control Systems (Proceedings of the International Multiconference on Computer Science and Information Technology pp. 849–859 ISSN 1896-7094)

Process and business layers robust integration (white paper)

Communication management in the Process Observer Archetype (Proceedings of the 16th conference “Polish Teletraffic Symposium 2009”)

Large Scale Distributed Process and Business Management Integration (Proceedings of the 14th International Congress of Cybernetics and Systems of WOSC)

OPC UA Makes Cloud Computing Possible.

For someone accomplishing hundreds of control system projects it is not easy to accept the fact that we have adopted most innovative solutions from business technology. Unfortunately, first a programmable calculator was produced and later after that the programmable controller (PLC) appears, first the personal computer (PC) was used to prepare invoices, and later after that SCADA was deployed on the PC. This post is about adoption of the Cloud Computing concept by the process control industry and requirements that must be fulfilled to apply safely this concept.

The cloud concept becomes more and more popular in the – we call them disdainfully – office suit, but more officially business management applications. Maybe it also could be widely adopted and will give us new arm to further improve manufacturing efficiency index including cost reduction and improve availability of utilities.

Applications are traditionally classified as:

  • Business management
  • Process management

Customers Relationship Management (CMS) is a business management application, but controlling a process using PLC is an example of process management. As a rule we do not try to discover relations and possibility to integrate functionality of applications like that. It is like a myth – they have nothing in common – that’s all. Really? Writing this sentence a concept of Smart Grid comes immediately into my mind, where optimization of energy consumption is located mainly on the customers’ site – energy consumers.

The above example is used to illustrate as the highly distributed measurement environment can be offered as a service.

Cloud Computing is defined as a method to provide a requested functionality as a set of services. There are many examples that cloud computing is really useful to reduce cost and increase robustness. Following the Cloud Computing idea and offering control systems as a service it is required a mechanism created on the service concept and supported abstraction and virtualization – two main pillars of the Cloud Computing paradigm.

In my opinion, it can be obtained as the result of set up this mechanism on the foundation of OPC Unified Architecture (see also OPC Unified Architecture – Main Technological Features) that is out of the box solution derived from the Service Orient Architecture principles. Therefore we can say that it is service centric solution.

Thanks to OPC UA standard we are able to abstract the process control as the OPC UA Address Space implementing selected, process oriented information model. Address Space is very useful to offer selective availability, as a means to manage the process representation and scope of its exposition to the users – OPC UA Clients.

In Cloud Computing concept the virtualization is recognized as possibility to share the services by many users. OPC UA server is a publishing mechanism exposing process data and meta-data to unlimited number of clients, and therefore it fulfills this requirement as well.

Multiuser dynamic and global environment causes a risk of unauthorized access and concerns about how cloud reliability and security could threaten manufacturing stability. Because OPC UA engages public key infrastructure – the strongest widely used authentication mechanism – the process can be protected against any cyber attack.

All the above lead to the sentence that process control community is well equipped to adopt the Cloud Computing and take advantage of new features that open new fields of applications. The only open question is if the process control community is ready to put trust on the new emerging technology.

See also: