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Home - How Google plans to disrupt IoT using edge computing
Data AnalyticsFundamentalsInternet of Things (IoT)

How Google plans to disrupt IoT using edge computing

by rcrwireless August 17, 2018
written by rcrwireless August 17, 2018 Share
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The Google Cloud Next 2018 conference took place July 24-26, bringing together the tech giant’s cloud leaders to talk about how the company is transforming businesses digitally. From the internet of things (IoT) through to artificial intelligence (AI), there was a lot of discussion around how Google Cloud was bringing new enterprise capabilities to harness IoT.

Injong Rhee, vice president of IoT at Google Cloud, wrote during the event that “there are many benefits to be gained from intelligent, real-time decision-making at the point where these devices connect to the network – what’s known as the edge.

“Manufacturing companies can detect anomalies in high-velocity assembly lines in real time. Retailers can receive alerts as soon as a shelved item is out of stock. Automotive companies can increase safety through intelligent technologies like collision avoidance, traffic routing, and eyes-off-the-road detection systems. But real-time decision-making in IoT systems is still challenging due to cost, form factor limitations, latency, power consumption, and other considerations. We want to change that.”

So how is Google planning to disrupt the enterprise IoT industry? Here, Enterprise IoT Insights looks at several ways which were announced during Google Cloud Next.

Edge computing

The Edge TPU, a new hardware chip, launched during the event and complements Cloud TPU and Google Cloud services. Bringing end-to-end AI infrastructure, the chip, which is smaller than a one pence coin, provides high performance with low power.

However, the Edge TPU isn’t just a hardware solution according to Google Cloud. It combines custom hardware, open software and AI algorithms, which the company considers easy to deploy.

The use cases for Edge TPU is growing. Google Cloud believes it can be used for predictive maintenance, anomaly detection, machine vision, robotics, voice recognition and more.

Machine learning

Cloud IoT Edge is a software stack and is said to be able to bring Google Cloud’s powerful AI capabilities to gateways and connected devices. It will extend Google’s capabilities to devices such as robotic arms, wind turbines and oil rigs, enabling them to act on data from the sensors.

This announcement is particularly exciting for the industry, as it means users can build and train machine learning models in the cloud. Google Cloud believes that this will increase operational reliability, allowing IoT solutions to be built on-premise without intermittent cloud connectivity issues, faster real-time predictions compared with general-purpose IoT gateways, and increased security for devices and data, ensuring data privacy and confidentiality.

Developer ecosystem

Google has always been known for opening up its ecosystem to developers, and its Cloud IoT offering is no exception. Rhee announced the Edge TPU development kit during the conference, with the aim of “jump-starting development and testing.”

This kit includes a system on module (SOM) that combines Google’s Edge TPU, a NXP CPU, Wi-Fi, and Microchip’s secure element in a compact form factor. It will be available to developers this October.

Rhee also confirmed that Google Cloud is working with its IoT ecosystem to develop intelligent devices that really reap the benefits of Cloud IoT at the edge. Semiconductor partners such as NXP and arm will create the SOM with the Edge TPU chip inside. Device makers such as Hitachi Vantara and Nokia will make the IoT gateways, and Edge computing partners include ADLINK Technology and Trax.

“Cloud IoT Edge, Edge TPU and Cloud IoT Core are opening up completely new possibilities with IoT,” writes Rhee. “With powerful data processing and ML capabilities at the edge, devices such as robotic arms, wind turbines and smart cars can now act on the data from their sensors in real time and predict outcomes locally. For IoT, this is the future.”

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Table of Contents

  • Edge computing
  • Machine learning
  • Developer ecosystem
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