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Network Infrastructure Magazine | Tuesday, February 14, 2023
IIoT allows for the better integration of data, systems, and processes to create real-time automated decisions and actions.
FREMONT, CA: As the industrial internet of things (IIoT) evolves, traditional, linear manufacturing systems become dynamic, interconnected ones that can unlock factories' potential to operate more efficiently, effectively, and predictably. The IIoT transmits data directly from legacy machines, sensors, and edge devices, driving operational intelligence, situational awareness, and predictive analytics directly from the factory floor. Front-line managers can take informed action, whether identifying problems in the supply chain beforehand or predictive maintenance of plant equipment.
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Although consumer connectivity, such as home automation and wireless communications, is often thought of as an offshoot of the IIoT, the industrial world adopted this disruptive technology first. Research shows that the market will continue on this trajectory over the next few years, reaching USD 1.1 trillion by 2028.
Artificial intelligence (AI): With AI, companies are increasing their use of IIoT data to increase operational efficiency, improve workflows, streamline logistics, improve safety, and optimize workflows. AI combined with IIoT data is helping predictive maintenance systems predict and prevent unplanned equipment downtime costs by millions of dollars. Research shows that the global AI market in the IIoT will expand at a rate of 27 percent from now until 2026.
Fog computing: As IIoT networks accumulate massive amounts of data, cloud computing is now considered invaluable because it allows companies to expand their storage capacity without hosting additional servers. Propagation and transmission delays can occur due to the distance between the cloud and IIoT devices. It is also possible for large computation loads to cause delays in processing and queuing on a single cloud server. Cloud-based solutions for the growing number of smart devices involved in the IIoT can be limited by bandwidth limitations and problems with scalability, speed, and computation. Fog computing pushes data and intelligence to analytic platforms near the original data source. A form of edge computing, fog enables real-time control, security, and management of devices at the edge. Decentralized, edge-driven IIoT networks are shifting away from centralized approaches to centralized management.
Big data analytics: Data generated by IIoT devices continues to grow exponentially, and big data storage and analytics allow us to make sense of it and gain valuable insights. Big Data Analytics (BDA) sorts vast unstructured data into smaller groups for better decision-making. IIOT insights will be available from BDA in descriptive, diagnostic, predictive, and prescriptive analytics. New machine learning algorithms and recent BDA innovations make real-time analysis solutions possible for managers to compare historical trends with future projections to predict future performance more accurately.
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