Many of us have yet to see an autonomous vehicle driving down the road, but it will be here faster than we can image. The car of tomorrow is connected, data-rich and autonomous. As 5G networks come online, sensors improve and compute and memory become faster and cheaper, the amount of data a vehicle will generate is expected to be 40 terabytes of data every day. This will make the autonomous vehicle the ultimate edge computing device.

Last week at Mobile World Congress Americas in San Francisco, Micron Technology hosted a panel discussion with automotive industry experts where they discussed the future of the connected car and the role of both the cloud and the edge in delivering the full promise of autonomous driving (FYI – Cars are now big at wireless trade shows. See Connected Vehicle Summit at MWC).

Experts from Micron, NVIDIA, Microsoft and Qualcomm discussed what 5G, cloud, IoT and edge analytics will mean for next-generation compute models and the automobile.

Micron claims to be the #1 memory supplier to the automotive industry and notes that its technology will be required to access the massive streams of data from vehicles. This data must be processed and analyzed, both in the car and in the cloud. Think about going down the road at 70 MPH in an autonomous vehicle. You need to have safe, secure and highly-responsive solutions, relying on split second decisions powered by enormous amounts of data. To quickly analyze the data necessary for future autonomous vehicles, higher bandwidth memory and storage solutions are required.

Smart, connected vehicles are the poster child for edge computing and IoT.

Some intriguing quotes from the discussion:

• “In last seven years 5839 patents have been granted for autonomous vehicle technology.” – Steve Brown, Moderator and Futurist

• “There is a proactive side of autonomous driving that can’t be fulfilled at the edge.” Doug Seven, Head of Connected Vehicle Platform, Microsoft

• “The thin client model won’t work for automobiles. You won’t have connectivity all the time.” – Steve Pawlowski, Vice President Advanced Computing Solutions, Micron

• “Once you have enough autonomous vehicles, the humans are the danger.” – Tim Wong, Director of Technical Program Management for Autonomous Vehicles, NVIDIA

The entire panel discussion can be found in the video below.

Disclaimer: The author of this post has a paid consulting relationship with Micron Technology. 

Guest post by Toby McClean

In 2016 Microsoft, IBM, and AWS each made concerted efforts to extend their IoT platforms to the edge. The main reasons for this are economics, physics and legal. Using the terminology defined in this paper; the edge is the hubs and devices in the system. In this article, we focus on the analytics capabilities that extend to the edge.

Descriptive Analytics

A descriptive analytics capability will identify what is happening. Descriptive analytics can be as simple as providing an alert if a value exceeds a certain value.

The IBM Watson IoT Platform provides an environment for defining rules that run in the cloud or on a hub at the edge. IBM announced the capability as part of the Cisco partnership and recently made it generally available.

The recent AWS Greengrass announcement allows for AWS Lambda behavior to run on a hub. A descriptive analytic is written in one of the languages supported by AWS Lambda.

The Azure platform mentions edge analytics here, but it does not provide any specific tools or extensions to existing analytics capabilities to run edge analytics.

Diagnostic Analytics

Why is it happening? Diagnostic analytics can help to determine why an alert is triggered and whether it is relevant or not. Often organizations use diagnostic analytics they develop the models for predictive analytics.

None of the three platforms offers the ability to run diagnostic analytics models at the edge. With AWS Greengrass, in theory, a diagnostic model could be developed as a Lambda and run at the edge.

Predictive Analytics

What will happen? The most common use case of predictive analytics is predictive maintenance. More and more use cases are attempting to predict positive outcomes. For example, analyzing parts that come off a production line to predict those parts that do not need further testing.

The three platforms provide cloud-based services to build and execute predictive models. However, none of them provides the ability to provision and run the predictive model at the edge.

ADLINK, IBM, and Intel collaborated on enabling predictive maintenance and quality models to run on a hub at the edge. For more information see,

Analytics provisioning, configuration, and management

Being able to build analytics models is fine. But, there is a need to be able to push those models to the parts of the system where it makes the most sense to execute them. For this article, we are concerned with the ability to provision the gateways or things in the system.

Provisioning of descriptive analytics to the edge can be configured and managed from the Watson IoT Platform. AWS IoT is fully capable of provisioning of Lambdas from the AWS IoT cloud to hubs or things running AWS Greengrass. For Microsoft Azure IoT, the public documentation does not reveal anything on this aspect.

Conclusion

The article has made no attempt to make any specific recommendations about which platform is better. Its goal is to provide the reader with information in order to help them make an informed decision for their specific use case.

Hopefully, you find it useful and please leave comments and suggestions.

This article originally appeared here. Cover Photo: Tomas Havel