Accelerating IoT device deployment with Google Cloud and Arm Mbed OS

For IoT to be successful and scale, IoT devices need to seamlessly communicate the data they capture to cloud services, where any additional compute capabilities cloud vendors provide can efficiently analyse the data and unlock business value. In this blog, we highlight how Mbed OS and Google Cloud IoT Core together provide developers with quick and easy access to a range of features and services to accelerate their IoT product. 

Mbed OS: simplifying IoT development

Arm’s key goal for Mbed OS is to simplify the development and deployment of IoT devices for software developers. Mbed OS provides a complete software platform OS for IoT that can be used with a wide array of hardware platforms. Arm has an active ecosystem of sensor vendors who provide software drivers that can easily be integrated with microcontrollers to quickly prototype and develop a fully functioning IoT device. Last year, Mbed OS announced the ability to connect to any public cloud, including Google IoT Cloud, providing developers more choice for building and deploying IoT devices.

A seamless development experience

Whilst Arm’s focus has been on the device side of IoT, no IoT solution can add value without accessing and using cloud services to unlock the value of the data captured by the device through the onboard sensors. The integration with Google IoT Cloud enables Mbed OS-based devices to securely connect and ingest data to Google Cloud through Cloud IoT Core. The data received by Cloud IoT Core is seamlessly forwarded to Google Cloud’s data analytics platform that comes with some of the most popular tools such as BigQuery, Dataflow, BigTable, and Looker for developers and data scientists to efficiently analyze, store, and visualize large amounts of data. These services are managed by Google Cloud and will easily scale with the amount of workload. Without needing to manage the infrastructure, development time is saved to focus on solving the problem and creating new solutions that deliver value for users and businesses.

Cloud IoT Core’s device management capability also enables control and configuration messages to be pushed to the IoT devices. By centrally controlling the devices with insights from data analytic processes, we can make an IoT solution data-driven and smart.

Integration architecture diagram.jpg
Integration architecture diagram

Intelligent IoT

In addition to our collaboration with Arm to simplify their IoT device development, our engineering teams are also optimizing support for developers building AI applications.

The combination of Google’s TensorFlow Lite for Microcontrollers (TF-Lµ) and Arm’s open-source CMSIS-NN library help developers achieve accelerated AI performance without having to do any additional work. The TF-Lµ framework is optimised for Arm Cortex-M processors and the Mbed development environment. Arm and TensorFlow team have recently announced improved benchmarks demonstrating a 4.9x performance uplift for a person detection example using TF-Lµ and CMSIS-NN.

Read more about the enhanced performance here.

In the future, we expect most AI processing to be delivered on-device, making technology more efficient, reliable, and secure. However, cloud connectivity will remain key. Using a predictive

maintenance machine in an industrial setting as an example, the machine learning model on-device will be trained to recognize an anomaly, with cloud connectivity being used to signal when a failure is imminent.

AI and IoT have the potential to ignite a new wave of creativity, and the work underway between Arm and Google Cloud will make it easier for developers to innovate the devices of the future.

Mbed OS and Google IoT Cloud: Get started 

  • Learn about the Cloud IoT Device SDK used in this integration on the github repository.

  • Follow this example integration to quickly configure and connect an Mbed OS-based device to Google services.

  • At the Mbed OS Tech Forum on Feb 10 this integration is discussed in more detail. Watch the episode here.

  • Read about the improved inference on Arm microcontrollers with TensorFlow Lite for Microcontrollers and CMSIS-NN.

 Learn more about Google Cloud IoT Core.

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