Category Archives: Big Data

What’s new about the Industrial IoT?

Cambashi has just completed a research project into the industrial application of the IoT. The aim was to establish the market’s structure and direction based on interviews with many of the major players combined with desk research. Many of the technologies that make up Industrial IoT are actually well established in their own right.

Most people believe the Industrial IoT is comprised of six layers, as the diagram shows. Let’s go through each layer in detail.



Mechanical parts
This can cover anything from vehicles to component parts. While most of the publicity has so far been around personal IoT devices, like fitness monitors and home appliance controls, our survey shows that industrial applications are growing faster.


Electronics, software, sensors, and actuators
These days, nearly every consumer and industrial item with a battery or an on/off switch includes software-controlled electronics (making it a “smart” product). These technologies have been around for decades.


The current trend is that component providers who offer all the digital metadata that describes their components may well have an advantage at this stage, because the metadata can feed into systems engineering and other tools, thus helping the project team structure, simulate, then plan the development project.

This is the means by which products communicate with the back-end systems and includes a range of methods from standard to proprietary. While it’s been possible to connect devices for some time, historically this has used proprietary, custom-built systems. Today, cloud computing provides a convenient, cost-effective way to connect.


Two other areas where innovation is improving connectivity are edge computing and evolving connectivity standards. In this case, edge computing can be defined as servers located close to the smart products or factories that act as a data collection point. Because vast amounts of data can be collected from the billions of devices in the field, it makes sense to do as much data processing as possible near the devices or sensors. This means that less data has to be transmitted to the cloud and less processing will be required later.

Evolving connectivity standards include the Industrial Internet of Things Connectivity Framework (IIC:PUB:G5:V1.0:PB:20170228) from the Industrial Internet Consortium (IIC). It lists ten core standard criteria ranging from “providing syntactic interoperability” to “having readily-available SDKs.” Against these, it rates four connectivity standards: DDS; Web Services; OPC-UA, and oneM2M.

Each of these standards is evolving to provide specific advantages in IIoT implementations. For example, DDS is a newer emerging, standard. Its key distinguishing feature is that, unlike the other three, DDS has no concept of messages; the software application talks to the data bus, thus providing a more efficient solution when the data has many destinations.

Product access and data routing
Almost every connected product has more than one organization interested in reading its data, and sending it commands. The product-access and data-routing layer controls and manages who has access to what. For example, a machine manufacturer and a third-party service company may offer machine monitoring, optimization, and predictive maintenance. What data will they see? What settings can they change? If something is changed, who is responsible for documenting the change and matching it to other records of use of the machine?


These data flows form a complex network, but it’s worth noting that product lifecycle management (PLM) systems have for many years handled access control to manage these kinds of data flows to and from design data. Repurposing and scaling this to cover all operational machines may not be straightforward, but PLM contains relevant experience of the necessary business logic and procedures.

Product-specific software applications
This is the heart of many new capabilities of smart, connected products. For example, a new capability to observe and analyze the status of a set of connected devices, and make a plan to operate or service them, will be provided by software in this layer. This layer also has the vital role of making appropriate connections and integration with other enterprise applications.


Other enterprise applications
Maintenance, repair, and operations (MRO) may well be the focus of a smart connected product initiative, perhaps a switch from fixing breakdowns to usage-based or predictive maintenance. But many MRO issues stay the same: fault handling; configuration; part or software availability for fix; schedule technician or online access to product; fix the problem, report the fix; share the know-how; customer acceptance.


The emphasis on the use of tools is mostly on tracking orders and configurations, scheduling technicians and parts for maintenance, and fault fixing. Good integration of these applications enables “servitization,” or enabling companies to supplement their products with additional services.

There’s little doubt that the Industrial IoT will continue to be disruptive, changing conventional business and software implementation models, and that the main elements, shown in the six-layer model are in place to support this. But there’s still plenty of room for innovation in the way the Industrial IoT is applied, the way smart-connected devices are developed and manufactured, and the capabilities of the tools and components used across all six layers of IoT.

Alan Griffith is Principal Consultant for Cambashi. His focus is to understand how engineering and manufacturing organizations use technical software applications to meet their business needs, and to assist the software companies who develop and market those applications with their market planning. He has a particular interest in the impact of cloud computing and the Industrial Internet of Things (IIoT). Alan holds an engineering degree from Cambridge University and qualified as a Chartered Engineer and PRINCE2 project manager.

via IOT Design

Pi in the Sky – Piday Art #ArtTuesday

Via Scientific American

Elegant new visualization maps the digits of pi as a star catalogue

The mind of Martin Krzywinski is a rich and dizzying place, teeming with fascinating questions, ideas, and inspiration. Krzywinski is a scientist and data visualizer whose primary line of work involves genome analysis for cancer research. In his spare time, though, he explores his many different interests as a scientific and visual thinker through creative projects. For the past few years, one such project has occupied him on a recurring basis each March: reimagining the digits of pi in a novel, science-based, and visually compelling way.
Today, this delightful March 14th (“Pi Day”) tradition brings us the digits of pi mapped onto the night sky, as a star catalogue. Like the infinitely long sequence of pi, space has no discernible end, but we earthbound observers can only see so far. So Krzywinski places a cap at 12 million digits and groups each successive series of 12 numerals to define a latitude, longitude and brightness, resulting in a field of a million stars, randomly arranged.

Read more and check out Martin Krzywiski’s website! You can purchase his artwork in poster form here!

Screenshot 4 2 14 11 48 AMEvery Tuesday is Art Tuesday here at Adafruit! Today we celebrate artists and makers from around the world who are designing innovative and creative works using technology, science, electronics and more. You can start your own career as an artist today with Adafruit’s conductive paints, art-related electronics kits, LEDs, wearables, 3D printers and more! Make your most imaginative designs come to life with our helpful tutorials from the Adafruit Learning System. And don’t forget to check in every Art Tuesday for more artistic inspiration here on the Adafruit Blog!


via Adafruit


According to a Rand report, doctors are expressing concern that current EHR technology interferes with face-to-face discussions with patients, requires physicians to spend too much time performing clerical work and degrades the accuracy of medical records by encouraging template-generated notes.