Are IoT Technologies Making the Grade? (Part 1)
18, July, 2017
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In the past few years, the Internet of Things (IoT) has swiftly moved from the era of pilot projects and proofs-of-concept to mainstream production. Companies are starting to run their businesses on IoT, not just experiment with it. While IoT is picking up steam, adoption is still limited by business structures, organizational culture, changing talent needs, integration with legacy systems, security, and fragmented standards. But what about the underlying IoT technologies? Are they keeping up with the accelerating demands of IoT?
Let's look at some of the technology transitions IoT needs to grow:
Analytics and Artificial Intelligence (AI) are the "secret sauce" of IoT. We are moving swiftly from the traditional model of centralized batch analytics to the real-time processing of data in motion. AI and IoT are emerging as perfect partners. IoT is both the source of real-time data for AI applications, and the means of executing AI decisions. While many AI applications are still in the proof-of-concept stage, it is already a transformative part of many production IoT applications. In fact, AI technology is the backbone of predictive analytics and predictive maintenance, two of four well-proven "fast-paths to IoT payback" I have identified. The Cloud-Fog Continuum is where data analytics does most of its work. In the traditional model, batch analytics took place in the cloud. Today, fog computing extends cloud capabilities to the edge of the network, where the data is generated. To save bandwidth and ensure real-time data processing, fog nodes can sort through mountains of data and send just exceptions back to the cloud for further analysis. In cases where latency is a problem, fog nodes can send real-time alerts—"drill bit is running hotter than normal"—so you can take immediate action. AI systems are moving in this direction as well. Once the logic is set, AI systems can run in specialized fog notes using FPGA, or even ASICs. This will reduce costs and accelerate adoption of driverless vehicles and other real-time AI solutions.
"AI and IoT are emerging as perfect partners. IoT is both the source of real-time data for AI applications, and the means of executing AI decisions. While many AI applications are still in the proof-of-concept stage, it is already a transformative part of many production IoT applications. In fact, AI technology is the backbone of predictive analytics and predictive maintenance.."
IoT Security burst into public consciousness last year when a distributed denial of service (DDOS) attack shut down major websites around the globe. That was a wake-up call for the industry. Today, all major vendors are investing in IoT security on par with other security domains. Security companies and industry groups are accelerating work on standards, interoperability, certification, and security education. Businesses are rapidly moving from "security by obscurity" (my plant is not connected and thus secure) into comprehensive policy-based security architectures. These must be built into every part of IoT operations, focusing not only on before (how I can prevent hackers to enter my systems), but also during (how quickly I can identify I have been hacked and what data has been compromised), and after (how I can remediate the problem). Chief Security Officers now own these architectures for both IT and operations, and the industry is actively developing solutions for new security use cases, such as vehicle-to-vehicle communication or new security paradigms for 24/7 operations.
In addition, IoT is becoming the foundation for the growing adoption of other groundbreaking technologies such as blockchain and drones.
Blockchain allows a secure exchange of value between entities in distributed networks. Bitcoin is perhaps the most famous application of blockchain technology. However, enterprise-grade blockchain offers a wealth of applications that go far beyond any digital currency. For example, an energy company is looking at blockchain to manage the interactions between solar panels and the power grid. Automakers are considering the technology to authenticate the interactions among connected vehicles. Blockchain creates a tamper-proof record of transactions, so it's ideal for tracing the source of goods throughout production and distribution. It can document food and drug safety, create smart contracts, and perform audits. Blockchain technologies (especially private, consensus protocol-based) are maturing quickly. We should see IoT production deployments later this year.
"Blockchain creates a tamper-proof record of transactions, so it's ideal for tracing the source of goods throughout production and distribution. It can document food and drug safety, create smart contracts, and perform audits. Blockchain technologies (especially private, consensus protocol-based) are maturing quickly. We should see IoT production deployments later this year.."
Drones have been over-hyped for their commercial possibilities, denigrated for their clandestine applications, and dismissed as high-tech playthings. But the Internet of Things makes drones business worthy, especially when combined with AI and fog computing. AI-powered autonomous drones can work longer and more efficiently than piloted drones. They can choose the most efficient flight path automatically, and can change it on the fly to avoid bad weather, trees, power lines, and other obstacles. Surveyors and map-making companies can use drones to document remote, rugged terrain. The scope of drone use is expanding rapidly from pipeline or cell tower inspection to warehouse inventory management.
The whole point of these technologies—and IoT itself—is to work together for business benefits. That's why standards are so important. Without standards, there cannot be interoperability. And without interoperability, benefits will be hard to find. The industry has been evolving rapidly from a collection of overlapping standards, semi-standards, specialized and proprietary technologies into true interoperable standards.
Such efforts have been focusing on three standardization thrusts:
-Interest groups in IEEE, IETF and other horizontal standards bodies are working to evolve existing horizontal standards to meet IoT requirements. Time Sensitive Networking in IEEE is a great example of evolving the Ethernet standard to meet manufacturing motion and safety requirements. This effort also meets in-car network requirements for level 3+ driverless vehicles.
-Vertical industry groups are migrating specialized or proprietary technologies to open standards. They are also standardizing foundational data fields essential for scalable data collection—for example, they are establishing a standard way to express "temperature" or "pressure" values. This effort is starting with controller-specific data and then moving to telemetry and diagnostics.
-Various consortia are developing frameworks and driving interoperability across their members' implementations. One example is the OpenFog Consortium, which released the OpenFog Reference Architecture earlier this year.
Bottom line: I would give the state of the IoT technology a B-. On the plus side, technologies are maturing, solutions are becoming interoperable, and we see a lot of scalable production applications. On the down side, IoT security adoption by both businesses and vendors is lagging, as is migration to open standards. Both of these are slowing down and increasing the costs of implementations. Time to study up!
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Maciej Kranz will be speaking at the IoT Tech Expo North America within the free conference track Developing for the IoT. His session 'IoT Reality Check: Starting and Scaling the Journey' will provide a proven step-by-step guide for organizations in almost any industry to start and scale their own IoT journey.
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