As the world experiences a quantum leap in the speed and scope of digital connections, industries are gaining new and enhanced tools to boost productivity and spur innovation.
Over the next decade, existing technologies like fiber, low- to mid-band 5G networks, low-power wide-area networks (LPWANs), and Wi-Fi 6—as well as short-range connections like radio-frequency identification (RFID)—will expand their reach as networks are built out and adoption grows. At the same time, new generations of these technologies will appear with upgraded standards. In addition, new types of more revolutionary and more capital-intensive frontier connectivity like high-band 5G and low-Earth orbit (LEO) satellites will begin to come online. Together, these technological developments will unlock powerful new capabilities across industries. Near global coverage will allow the expansion of use cases even to remote areas and enable constant connectivity universally. Massive IoT advances will be enabled as new technologies allow high device densities, and mission critical services will take advantage of ultra-low latency, highly reliable, and highly secure connections.
From healthcare and manufacturing to mobility and retail, there are hundreds of promising use cases for the emerging generation of enhanced connectivity. Together, advanced and frontier connectivity could help seven sectors add a total of $2 trillion to $3 trillion in additional value to global GDP.
IoT Analytics, a leading provider of market insights & competitive intelligence for the Internet of Things (IoT), M2M, and Industry 4.0, today published a comprehensive Market Report, focusing on sizing the quickly developing market for Connected Streetlights during the period 2018 to 2023. It is estimated that there will be 41 million IoT connected Streetlights installed globally by 2023. The overall streetlights market will surpass US$3.6B in 2023, growing at a compound annual growth rate of 21% from 2018. Deployment of connected streetlights is gaining traction globally as the technology is one of the key pillars for Smart City initiatives. The growth is fueled by government policies and increasing awareness on the benefits of connected Streetlights which go beyond energy savings.
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.