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AT&T to Launch NarrowBand IoT Network in U.S. and Mexico
KEN BRIODAGH | July 2, 2018
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Article | February 28, 2020
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.
As industries invest in the next generation of technology, the internet of things is at the top of the list. Once a novelty best known in residential settings, IoT technology has become mainstream in business and industry. Some even predict that having a Google Home or Amazon Echo in the conference room will become the norm. Now, you will find connected devices in Amazon’s warehouse robotics system Kiva, Airbus’ Factory of the Future and the augmented reality used by Caterpillar in its Cat Connect system. The number of IoT devices in use by 2025 could reach 22 billion, according to an IoT Analytics report. They will transform manufacturing, warehouse and logistics, health care, agriculture, shipping, energy and aviation. IoT security can’t be done as an afterthought; it must be integrated into operations from Day One because connected devices and systems use cryptographic keys that transmit data. Cybercriminals can gain access through these keys, allowing them to infect devices and systems with malware, steal data or shut down systems. Sometimes, the vulnerability has gone undetected for months.
To paraphrase a well-known saying, the journey to a complete smart city begins with a single building. No matter the size of the city, the extent of the technology or the most helpful use cases, a prospective smart city can integrate into — or branch off of — initiatives pushed forward by a smart building or campus. And when there is an increasing demand for these types of solutions, large corporations have the opportunity to improve corporate and social governance practices, as well as stand out in their community by championing more connected technologies.
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.
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