PrismTech Presentation Highlights how OMG DDS Standard is a Crucial Enabler for the Industrial Internet of Things (IIoT)

At the recent Object Management Group (OMG) Technical meeting in Cambridge, MA, USA, PrismTech SVP for Corporate Development, Steve Jennis presented on the subject of “Data Distribution Service (OMG DDS) – Aligning OT and IT to Deliver the Potential of the Industrial Internet of Things (IIoT)” at its special Putting IIoT to Work Event.

The presentation highlighted the four mega trends that are driving IIoT adoption, specifically: (1) the continuous decline of the cost of Internet-connected sensors, (2) the vast amount of money being invested into the IoT by both large and startup companies focused on the development of devices, gateways, software, cloud, fog nodes, analytics and HMI technologies, (3) almost ubiquitous Internet connectivity and (4) the high adoption levels of smart phones, tablets and other BYOD devices.

The presentation also discussed how the OMG DDS standard can support the creation of new end-to-end IIoT systems that provide the potential for innovative new products and services, higher levels of productivity and environmental benefits.

Finally Steve showed how PrismTech’s Vortex, the leading commercial and open source implementation of the OMG DDS standard, is being used as a data-centric cross-platform enabler for OT / IT integration between edge, gateway, cloud, mobility etc. and how it is helping deliver OT and IT alignment, and thus the IIoT.

IoT in Oil and Gas 2015 – Houston TX, USA

PrismTech SVP, Steve Jennis will be presenting on “The Potential of the IoT: How will it change our business lives”

Smart Industry Conference and Expo


Date: 16th September 2015 to 17th September 2015
Location: Houston, TX, USA

The Internet of Things is revolutionizing oil and gas. The era of lower oil prices is here – making it crucial for operating companies to focus on operational efficiency and resource utilization. Energy companies are increasingly looking at how to harness their data across their supply chain by connecting their “things” – the people, processes and assets. Huge investments have already made in technology across operations – now the key is to leverage these investments to create new efficiencies and cost savings.

This is the first conference in the world to focus on how oil and gas companies can use IoT to make their operations:

  • More efficient
  • More secure
  • More safe

This conference is the perfect platform for technology innovators to educate the market about how their solutions can be the panacea to their technology challenges.

IoT in Oil and Gas is not a talk-fest. It is not a series of product pitches. It is a outcomes-driven gathering of innovators and buyers that will focus on:

  • Real world case studies from operators and service companies already seeing the benefits of IoT.
  • One to one meetings and networking through our unique Conference Connect platform – before, during and after the events.

IoT in Oil and Gas will be focusing on the key areas of:

  • Big data
  • Cybersecurity
  • Automation
  • Real-time analytics
  • Industrial control networks
  • HSE and process safety

The conference is a must attend for technology, engineering and automation professionals wanting to keep pace with rapidly developing technology.

For further information about the conference including registration details please visit:

Vortex OpenSplice – Power at the core of your IoT Systems

PrismTech features Vortex OpenSplice on its latest video highlighting its exceptional properties such as power, performance, scalability, efficiency, data centricity, security and quality-of-service.

From the original OpenSplice DDS product, Vortex OpenSplice is a combination of innovation, improved performance and the most comprehensive DDS implementation available in the market. Vortex OpenSplice enables data to be shared and integrated across a wide spectrum of operating systems and platforms; and has been successfully deployed in many missions and business-critical systems.

Vortex OpenSplice is available for a FREE 30-day trial. Click here to download.

Beyond M2M to Enterprise IoT

Figure 1. A Layered Enterprise IoT System Architecture
Figure 1. A Layered Enterprise IoT System Architecture

Applications running on edge-devices, gateways, enterprise servers, cloud services and mobiles are all valuable data sources and sinks in an IoT world.  But new software platforms are needed to connect and leverage all these sub-systems to maximize the business value-add of Enterprise IoT.

For several years, M2M platforms have provided reasonable solutions for connecting machines to cloud services (actually it should be M2C, as M2M platforms generally do not support peer-to-peer device communications).  But these platforms have struggled to create large markets or provide strategic enterprise-wide solutions.  They have mostly been restricted to providing vertical/tactical applications — in effect self-contained ‘stovepipe’ systems.

But to fully exploit the potential of the IoT, data must be free to flow to wherever in the system it can add value, e.g. between ‘edge’ devices for control purposes, to gateways for data aggregation/ingestion and local analytics, to cloud-based applications for Big Data analytics, to enterprise systems for OT/IT alignment and supply-chain integration, to mobiles for on-demand data delivery to employees (see Figures 1 and 2).  The promise of Enterprise IoT is the new value created through ubiquitous data availability (and its processing by applications into actionable insights), but this means a new generation of platforms is required to provide the data-connectivity to support a new generation of distributed IoT applications.

One of the biggest differences between traditional M2M and Enterprise IoT systems is that ‘horizontal’ as well as ‘vertical’ data-flow must be supported.  Vertical silos of data do not provide the potential to add value beyond a specific sub-system, so a fundamental feature of next-generation IoT platforms will be a data-connectivity layer that supports system-wide data-delivery as required: the right data, in the right place, at the right time, system-wide.

There are many potential ways (control, analytics, dashboards, event processing, mobile apps, etc.) to exploit all this newly accessible IoT data, but it needs to be delivered to the appropriate application in a timely manner wherever in the system that application may reside (on an edge device, gateway, enterprise server, tablet, or in the cloud).  Only then can the data be converted into new ‘actionable insights’ and thus new business value.

Figure 2. End-to-end IoT System Functionality: Providing intelligent data-connectivity for end-to-end systems embracing Things, gateways, enterprise servers, cloud services, mobiles, etc. to support Enterprise IoT solutions.
Figure 2. End-to-end IoT System Functionality: Providing intelligent data-connectivity for end-to-end systems embracing Things, gateways, enterprise servers, cloud services, mobiles, etc. to support Enterprise IoT solutions.

To provide this underlying capability, a data-connectivity layer needs to be deployed across all nodes the in the system — at least all the nodes that are required to share data (publish and/or subscribe).  An enterprise version of Twitter for Things, in effect.

In simple terms, the diagrams in Figures 3 and 4 show, respectively, how this layer can be deployed both in the cloud (to support cloud services) and on devices (Things, servers, PCs, mobiles, etc.).  They also show potential sources of the applications the platform connects (end-user developers, ISVs, SIs, OEMs).

Figure 3. IoT Cloud Services Environment: PrismTech's Vortex provides the intelligent data-connectivity between the functional components within a cloud PaaS offering for Enterprise IoT solutions.
Figure 3. IoT Cloud Services Environment: PrismTech’s Vortex provides the intelligent data-connectivity between the functional components within a cloud PaaS offering for Enterprise IoT solutions.
Figure 4. IoT Edge-Device Environment: Similar to the PaaS offering, PrismTech's Vortex provides the intelligent data-connectivity between functional components in an IoT device and other devices, sub systems and cloud services for Enterprise IoT solutions.
Figure 4. IoT Edge-Device Environment: Similar to the PaaS offering, PrismTech’s Vortex provides the intelligent data-connectivity between functional components in an IoT device and other devices, sub systems and cloud services for Enterprise IoT solutions.

[Note that the data-connectivity layer supports not only inter-node data-sharing, but also data-sharing between the application components of the IoT platform itself, i.e. inter-operability between platform services (such as IDEs, edge-device management, API management, analytics engines, etc.) as well as between Things].

To read the full article, visit

Juggling Data Connectivity Protocols for Industrial IoT : Andrew Foster Reports

Real-time needs are key in multiprotocol industrial IoT.

With much legacy equipment existing with older protocols and requiring diverse real-time needs, the Industrial IoT will not soon, if ever, use a single data-connectivity standard.

 The projected benefits that can be gained from the Industrial Internet of Things (IIoT) have been well documented during the past several years by the likes of General Electric, Accenture and other organizations that have conducted extensive research in this area. In fact, these benefits in revenue, cost reductions and energy consumption are now coming to fruition in a variety of smart city, smart farming, transportation and other industry sectors.

A great example is the Connected Boulevard program in Nice, France, which uses Industrial Internet technologies, including an innovative data-sharing platform, to help manage and optimize all aspects of city management, including parking and traffic, street lighting, waste disposal and environmental quality.

The key to these benefits is the ability to derive value from the data. The data must be accessible wherever it resides and delivered to wherever it’s needed (edge to the cloud) so that it can be analyzed and acted upon in the right amount of time. There are a range protocols currently used to provide this “data-sharing function” within an Industrial Internet system (see chart above). Chief among them are:

  • The Object Management Group’s (OMG) Data Distribution Service for Real-Time Systems (DDS);
  • OASIS’ Advanced Message Queuing Protocol (AMQP);
  • MQ Telemetry Transport (MQTT), a protocol originally developed by IBM but now an OASIS standard;
  • Representational State Transfer (REST), a common style of using HTTP for Web-based applications and not a standard; and,
  • Constrained Application Protocol (CoAP), a software protocol to be used in very simple electronics devices such as Wireless Sensor Networks (WSN) that allows them to communicate over the Internet; and,
  • The eXtensible Messaging and Presence Protocol (XMPP), the IETF’s formalization of the base XML streaming protocols for instant messaging and presence technology originally developed within the Jabber open-source community.

To read the full report, visit

In the age of IoT, think “Data Centric”!

Data Centricity helps creating situation awareness to manage and control complex systems and systems of systems in an Internet-centric World.

In a smart city case it can help you building a system managing green smart houses and smart buildings, managing traffic, managing parking, garbage collections, energy and so one.

Unlike messaging, in a data centric based system each entity in the real world is truly represented as a data object that has :

  • An identity
  • A Structure
  • A State
  • A Lifecycle, and
  • Some Meta data that characterises it and captures all the above

Data qualities can even be attached ( such as Security, Persistency, Consistency etc …) to the data objects.

Take a Car as an example, a car is a pretty complex system that can live, circulate and evolve in other systems such as traffic mgmt. system or within a smart city system.

A car has:

  • An Identity :
  • A Status :

– It can be moving ,

– It can be Broken

– It can be in Maintenance:

– It can be Parked :

Or .. It can be scrapped:

A Data Centric system can instantaneously tells you which “new” cars have been observed or those that have left or been “disposed” from the system. It does all this with no extra effort because a Data Centric system “knows” each data entity individually and does automatic assertion of their liveliness.

  • A Lifecycle :

Being aware of the data lifecycle allows you to automate for instance your system resource management by releasing all the other objects that are associated to your data and releasing memory, threads, files etc … they are using .

Let’s now try to analyse a car structure by looking to its anatomy…. but more importantly by seeing it as an entire system.

  • A Structure

A car has a complex structure. Representing its structure is tough if you are not using a powerful data description language that captures not only subsets it is made of but also the relationships between them.

A Simple car will be made of :

  • A bodywork ….
  • 4 wheels ……..
  • Many many sensors
  • On board computer
  • Brake system …and
  • An Engine …

The engine is a subsystem itself yet is made of many other complex sub-systems:

  • A Carburettor
  • Pistons
  • Pushrods
  • Fan
  • Timing chain
  • Etc >>>>

Each of those subsystems can be captured as individual data objects:

Your data representation can be strongly structured or it can be loosely or semi-structured.

In Data centric world, you can apply and enforce business rules, for instance, the engine will function correctly only when all its sub systems are in a steady state working properly. Furthermore, Data centricity can capture relationship between data objects and entities:


A Driver is first of all a Person, that has an identity, a state and a structure. He can own zero, one or several vehicles and cars, but unfortunately for the time being he can only drive 1 vehicle at a time !!!

All those data entities and their relationships can be captured naturally in a Data Centric system and they live in harmony in a Distributed Global Data Space (GDS).

Nowadays big and sensitive Data is coming from everywhere, data producers and the data consumers are distributed over Local, Wide area , Mobile networks or on Internet or even on ad-hoc networks. In a nutshell we can say that the Network and Internet are becoming a tremendous data space and certainly a huge gold deposit !!

Let’s say you are the owner of a “moving company”, …….

To better manage your fleet vehicles and trucks you decided to install a GPS tracking device on each vehicle of your fleet coupled with an “engine diagnostic box” provided by a cutting edge automotive supplier that enables remote diagnostic and assistance… .

Such system would not only help in knowing where your drivers are in a real-time manner but it will also allow you to assist them in case of an accident or if any truck is broken.

A scenario where Data Centricity Excels over Messaging

Let’s take the case of one driver that in a beautiful and sunny day takes his track to deliver some goods and products. While he is driving …His Truck brakes down in the middle of nowhere !!!!!!!!!! a disaster !!!!!

  1. Fortunately, the truck position is constantly reported by the GPS device and from time to time it reports the engine status compressed over mobile networks
  2. When the engine is broken its diagnostic box will update urgently the engine data graph that has been built over time and change its status to “broken” . Instantaneously, all the interested parties will be notified of “Engine status change”. Some diagnostic applications will immediately start the data analysis to identify the broken subsystem (c.f fig below), while others will be in charge of alerting you by SMS seeking for your assistance as the boss of the company in case there are some extra fees to pay. Some of those diagnostics and analysis applications will require assessing the overall engine data object, while others, more specialised, will only be looking at a subset of the engine data objects. What makes a Data Centric System unique compared to other design paradigm is that each application can have its own view on the data.
    In a very near future, such diagnostic and analysis will happen likely in the Truck provider’s “private cloud” … Such data centric system will require a technology and platform that maintains virtually a unique single dataspace yet where that dataspace will necessarily be physically distributed.
  3. If the failure cannot be fixed remotely, (by for instance restarting the engine in a degraded mode operations or disabling some automatic assistance systems (e.g the shield safety system , or restarting the on-board computer etc ….. ), a (human) expert with the required expertise will get involved for further analysis remotely .
  4. Let’s assume it is the alternator that is broken , a spare part will be immediately ordered …and
  5. an assistance vehicle will bring it to the broken truck minimizing the overall response time !!

Messaging oriented Vs Data Centric System, the Conclusion

With a Message centric system, things will be more complex to build, as …fundamentally…. a message is just an information container, with usually a header and some payload. Messaging will break the overall data representation of the system and you will lose the relationship between them !. With a messaging based system, application logic will need to rebuild each time the full picture of the system from unrelated individual pieces of basic information captured within messages and recreate the full picture and also by manually assuring the consistency of the overall picture. This process can be terribly complex, time consuming and error prone.

On the other hand, Data Centricity inherently rebuilds and guarantees consistency of the overall picture. It helps you getting the system situation awareness you are looking for free.

Data centricity is a very powerful paradigm that supersedes messaging as it can at the same time:

  1. Models Real-world entities as they are with their unique identity , state, structure, meta data and lifecycle
  2. Catchs and represent the relationship between data entities
  3. Helps implementing a decoupled systems where applications interact only by sharing data and exchanging information.
    • A Data Centric based platform will be in charge of maintaining the state of the overall system even in case of failure so that the latest consistent state of the system will always be known and will always be available from late joining applications, whatever their access point to the system will be.
  4. Data Centricity is generic enough and polymorphic to model other interaction paradigms, as it can also model :
    • Event and notification based communication ,
    • Conversational protocols , such as Request / reply based protocols in an abstract and efficient way. This will help part of the system -if needed- to be also Service Centric (to implement SOA based systems too).
    • Lightweight Transactional communications based on the so called coherent -sets and eventually consistent models
    • Message oriented communication -if needed- by a subsystem

Data Centricity is nowadays backed by an extremely powerful Real time Pub/Sub standard and interoperable Middleware technology ( that is able to AUTOMATICALLY Discover all the Data entities whenever and wherever they are. You can now Build, Share and Monitor data distribution everywhere from embedded devices to machines and Servers in private and public Clouds or in internal company domains.

Data Centric Middlewares connect people at work whether they are, using their workstations, smartphones or tablets to the heart of any mission critical system, securely, efficiently in any circumstances.

So in a nutshell, the key lesson here is ……if you hear someone comparing Data Centric Platforms using the standard Data Distribution Service (DDS) to a Message oriented Middleware (M.o.M) tell him he is doing as if he is comparing a Data base system to a File system, or more precisely, comparing a Distributed Data Base to a Network File System!..

At the end, always remember that in the IoT Age, Data is certainly becoming your strategic economic value, its your key factor of growth and success ! Treat it appropriately!!