Author
Monica Davis
Monica Davis writes about technologies and industry challenges that shape security and edge topics.
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- Empowering the Edge Everywhere
Empowering the Edge Everywhere
Not too long ago, IoT was an acronym for something so wide and broad that it was almost undefinable. Even its pioneers had a hard time describing it in other than equally broad terms. We’ve come a long, long way since then, including several groundbreaking trends.
One of the most important trends is focused on the process of turning raw data into actionable intelligence. Until recently, this was only possible in the cloud with its huge data centers. As IoT evolves, it is becoming obvious that moving the increasingly enormous amounts of data generated by sensors from the end-user location to the cloud and back is no longer viable. A better approach is to complement the cloud by performing more of this processing at the user location—at the edge.
Edge computing not only keeps critical data local, but also reduces round-trip latency (a necessity for real-time applications) and lessens the burden on communications infrastructure. And with a pre-trained machine learning model running on an intelligent edge processor, an IoT device can take real-time decisions locally. Even with no connection, your smart door lock unlocks when it recognizes your face because the machine learning runs on an edge device where your personal images are stored. And your private data, including your comings and goings, can stay at the edge without leaving your home.
The pace of edge computing is advancing so fast that today’s “breakthrough” will be eclipsed a short time later by yet another breakthrough. And while we see traces of early IoT concepts in devices we use every day, innovations in edge computing are rapidly transforming.
Today, intelligent edge solutions are being deployed on equipment of almost every kind, from next-generation wearables to huge industrial robots, and can perform functions as diverse as facial and object recognition to anomaly detection. The next step is to enable even greater intelligence in these devices to form what NXP calls the aware edge, where devices within a closed edge network are capable of securely sharing data—with or without the help of a cloud. It’s here that analysis is performed in a more human-like way for devices to analyze the environment, make decisions and even act on them.
The edge has great potential to change how we interact with our world in a more productive, safe and efficient way. From a technology perspective, what do you need to have? What role does the industry play in helping ensure ethics in AI? What challenges remain with edge technology?
Join EE Times and Embedded.com Editor Nitin Dahad and NXP’s Ron Martino, senior vice president and general manager of the Edge Processing business, for fresh insights on the emerging technology for IoT and industrial edge applications.
Listen to the latest episode of the Smarter World podcast for insights on the intelligent edge.
You can subscribe to the podcast on Spotify.
