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NB-IOT Enabling New Business Opportunites
| March 1, 2016
SMARTSIDE provides Information Technology solutions for Smart Metering and Smart Grids. Using the best technologies out there, SMARTSIDE develops an innovative solution dedicated to utilities, energy services providers.
Article | February 18, 2020
IoT is huge, but AI has the potential to supercharge the technology even more. Gartner predicts that this year 80 percent of all enterprise IoT projects will include AI. One area where many have been critical of IoT is a lack of good security. When coupled with AI, that could be improved, but some worry that AI can also be used as a tool to defeat IoT security. Dean Chester, cybersecurity author, wrote that “AI can be used for IoT security, or it can be used against it.”Melvin Greer, Chief Data Scientist, Intel Americas , said that “AI and IoT are no longer in separate swim lanes. AI closes the loop in an IoT environment where IoT devices gather or create data, and AI helps automate important choices and actions based on that data. Today, most organizations using IoT are only at the first ‘visibility’ phase where they can start to see what’s going on through IoT assets. But they’re moving toward the reliability, efficiency and production phases, which are more sophisticated and require stronger AI capabilities.
Tech companies are stepping up Internet of Things technologies to protect against COVID-19 and future viruses by using LiDAR and infrared cameras to detect a person’s body temperature from a distance or even handwashing. Keeping the data secure in such detection is also going to be a challenge. One approach is to put a chip inside an IoT device when it is manufactured to enable strong authentication and secure communication, mainly to guard against device counterfeiting. Hitachi Vantara has touted forward looking infrared cameras (FLIR) cameras to detect the temperature of a person from a distance. That way a passenger on a train or a worker or a customer in a store can be non-intrusively screened, according to a blog from Mark Jules, global vice president of smart spaces and video intelligence.
While the Industrial Internet of Things (IIoT) is maturing rapidly, there’s often a strong link to Artificial intelligence (AI) and Machine Learning (ML) tools to manage data flows. This raises a host of challenges including connectivity, security, storage and modeling requirements. Designers of IIoT devices and larger deployments must build in contingencies for shifts in these areas or risk falling short. While IIoT is gaining in maturity, the overall market still has many challenges to overcome. Some of these challenges are technical barriers to IoT device operation, such as connectivity standards and battery technology, but the picture is even wider than that. The world of IIoT not only requires IoT hardware to operate reliably and efficiently in all environments but also relies on supporting networks and services too. The result is that IIoT deployments cannot be viewed as isolated technology upgrades or productivity drives, but highly interconnected ecosystems sensitive to a wide range of variables – in particular the developing world of AI.
At IDC I co-founded a practice that studied the three dimensions of IoT: industry technology platforms, industry verticals and industry geographies. As an industry analyst, I had a front-row ticket to the IoT market and continually witnessed incredible innovations. Like many other experts, I kept my seatbelt buckled for what we expected to be IoT’s massive and dramatic take-off. While there has been uptake, real-world adoption and implementation have lagged the “hype.” In time, I found myself growing increasingly frustrated at the disconnect. What was the holdup? Knowing that industry analysts are beholden to the marketing messages that each company shares, I welcomed an opportunity to make the move from an outside-in analyst to an inside-out contributor.
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