Machine Learning and IoT

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Machine learning has experienced a boost in popularity among industrial companies thanks to the hype surrounding the Internet of Things (IoT). Many companies are already designating IoT as a strategically significant area, while others have kicked off pilot projects to map the potential of IoT in business operations.

As a result, nearly every IT vendor is suddenly announcing IoT platforms and consulting services.

But achieving financial benefits through IoT is not easy. The lack of concrete objectives is disconcerting. The advancement of digitization and IoT places new prerequisites on both buyers and sellers. Many businesses have failed to clearly determine what areas will change with the implementation of an IoT strategy.

In other words, clearly defined, concrete intermediary objectives are missing. For example, industrial companies produce a massive amount of data on a daily basis. However, by and large, companies fail to systematically collect, store, analyze and use such data to improve process efficiency or meet other goals.

Furthermore, not many vendors are able to establish, in concrete terms, to the client how to prudently create positive impact on business operations with IoT solutions. Simply the promise of a cloud-based IoT platform is not enough.

2017 IIoT Security Survey

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2017 IIoT Security Survey

“Industry professionals know that the Industrial Internet of Things security is a problem today. More than half of the respondents said they don’t feel prepared to detect and stop cyber attacks against IIoT,” said David Meltzer, chief technology officer at Tripwire. “There are only two ways this scenario plays out: Either we change our level of preparation or we experience the realization of these risks. The reality is that cyber attacks in the industrial space can have significant consequences in terms of safety and the availability of critical operations.”

Technavio – Global IIoT Sensors Market in Oil and Gas Industry 2018-2022

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Market analyst firm Technavio has a new report that provides a comprehensive analysis of the global IIoT sensors market in oil and gas industry by product such as temperature sensors, flow sensors, flow sensors, pressure sensors, and other sensors. The report also provides a comprehensive analysis of the growth opportunities for companies in this market in regions such as the Americas, APAC, and EMEA.

The growing focus of the oil and gas industry on reducing the cost is encouraging them to adopt IIoT sensors as the installation of these sensors takes less time. In addition, the sensor manufacturers are increasingly offering sensors with easy assembling options and technical advancements. Moreover, the rising competition among major manufacturers of sensors and service providers of IoT products is increasing, which in turn, will boost the adoption of these sensors in the oil and gas industry. Research analysis on the global IIoT sensors market in oil and gas industry identifies that the growing commercial acceptance of IIoT sensors will be one of the major factors that will have a positive impact on the growth of the market. Technavio’s market research analysts predict that the market will grow at a CAGR of more than 5% by 2022.

The preference for industrial internet of things enabled smart asset monitoring solutions add intelligence to automated workflows, real-time alerts, dynamic edge control of assets, cross-domain analytics, insights from data, real-time visibility, and predictive maintenance. This is driving the adoption of smart asset monitoring, which in turn, is identified as one of the key trends that will stimulate growth in the IIoT sensors market in oil and gas industry throughout the projected period.

GE – The Industrial Internet of Things for Developers

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GE recently launched a new book, Industrial Internet of Things for Developers, that explains much of what needs to be understood by those interested in and tasked with developing applications for the Industrial Internet of Things (IIoT). Foremost among these is that if you are going to create applications for the IIoT, the development process must change.

Forbes technology contributor Dan Woods provided a review of the book, highlighting how it helps orient developers to the unique elements of IIoT by focusing on four areas:

  • Systems at the edge. Developers must understand the different characteristics of edge systems, which may be anywhere: on high-speed rail, under the ocean, in a factory, down a mine shaft, and more. The book shows the differences between OT and IT applications, highlighting how much more static the OT world is. With OT, the top priority is always to keep operations going, rather than embracing rapid change. Developers will have to understand fully how these edge systems function before being able to build effective apps.
  • Changes to platform development. The book also suggests that platform development must change. In the traditional world of development, full stack developers build applications from the ground up. But there is a new stack on the edge and if you’re going to build applications on the edge that connect centrally, you need a platform that can communicate from the edge to the cloud. That platform must have a distributed architecture, with end-to-end security and be able to handle the unique types of data edge devices generate. Data must be able to flow from the edge to the platform, where it can be transformed and analyzed. But control of the entire process must be able to flow down from the enterprise level, through the platform, to the edge. Security is important, because edge devices are especially vulnerable.
  • Digital twins. To create applications that provide an ability to understand what’s happening with advanced equipment, companies will need to create digital twins. These are electronic doppelgangers of physical equipment that use sensor-generated data from that equipment to create digital replicas. With digital twins, you can monitor the physical world digitally. And you can do so on whole fleets of equipment. In the past, an operator would put his hand on pump and see if its vibrations indicated it was working improperly. With digital twins, you can put a digital hand on every pump at all times to see how everything is operating, and see the relationships between all your equipment and devices. The result is a much improved ability to assess the functioning of your equipment, as well as the ability to optimize your systems, and detect problems proactively, rather than reactively. IIoT applications must be able to incorporate digital twin data.
  • New IIoT development teams. The book also highlights the need for companies to create different teams to build IIoT applications than they have had for applications in the past. Instead of creating applications with just an app developer and a business analyst, you need people who are the experts in the edge environment and the many standards and protocols there, full stack developers, domain experts who understand the physics of the equipment and can help create digital twins, and data scientists who can analyze the data generated by it. This brings in a wider range of skill sets. Assembling such a team is only the first step—you have to package their accumulated knowledge into components that can be used over and over again across the business. In addition to using traditional development techniques, these teams will have to be able to knit together applications with low code environments, as not every person involved in application development will have extensive coding skills. Finally, the development process—particularly as it relates to voluminous edge data—will also become more automated and assisted by machine learning.

Cisco – Patent Application for Blockchain, IoT Integration

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Cisco has recently filed a U.S. patent application for an invention that it describes as a, “Block Chain Based IoT [Internet of Things] Device Identity Verification and Anomaly Detection.”

The concept has to do with enabling a blockchain-based system that could record changes to the conditions affecting and captured by sensors (i.e., smart objects) in a network and instrumentalize network relationships and the data that the network generates in order to exercise control over those nodes.

The application lists “the smart grid, smart cities, and building and industrial automation” among the types of Low-Power and Lossy Networks (LLNs) that might operate more efficiently with the integration of the invention. The smart objects/sensors that could, at least partially, comprise these networks include “lights, appliances, vehicles, HVAC (heating, ventilating, and air-conditioning), windows and window shades and blinds, doors, [and] locks,” as well as actuators – automated devices that can, for instance, start an engine.