https://en.wikipedia.org/wiki/Fog_computing

Announcing AWS Wavelength for delivering ultra-low latency applications for 5G
https://aws.amazon.com/about-aws/whats-new/2019/12/announcing-aws-wavelength-delivering-ultra-low-latency-applications-5g/

AWS Wavelength embeds AWS compute and storage services at the edge of telecommunications providers’ 5G networks, and provides seamless access to the breadth of AWS services in the region. AWS Wavelength enables you to build applications that serve mobile end-users and devices with single-digit millisecond latencies over 5G networks, like game and live video streaming, machine learning inference at the edge, and augmented and virtual reality.

AWS Wavelength brings AWS services to the edge of the 5G network, minimizing the network hops and latency to connect to an application from a 5G device. Wavelength delivers a consistent developer experience across multiple 5G networks around the world

Cloudflare to open an edge computing service for developers using its network
https://www.geekwire.com/2018/cloudflare-open-edge-computing-service-developers-using-network/

Cloudflare is ready to take the wraps of a new service designed for developers creating Internet-of-Things apps that want to capitalize on the proximity benefits provided by edge computing.

Cloudflare Workers was first introduced last September, and Cloudflare is expected to announce Tuesday that it is now generally available for developers to check out. The new service runs on hardware that Cloudflare has installed in more than 125 data centers around the world to power its anti-DDoS (distributed denial of service) attack service, and it allows developers to write JavaScript applications through the Service Worker API that will run much closer to their users than might otherwise be possible with standard cloud services.

“For quite some time, we have understood that there is real power in deploying applications that ran incredibly close to where users are on the internet,”

About 1,000 users have been playing with Cloudflare Workers since the company opened the service up to a broader beta program in January following the September announcement. “I’ve been surprised by how dramatically different all of the applications people have bult are, it doesn’t feel like there is a bound to them yet,” Prince said.

The benefits of edge computing are just starting to make their way into the world, although lots of folks have been talking about it for a while. It’s a recognition of the fact that as connected devices spread throughout the world, it quickly makes more sense to execute a lot of the code running those devices as physically close to them as possible, as waiting for instructions from a remote cloud data center won’t always cut it for real-time IoT devices.

Real time has taken on a new dimension with the advent of Industry 4.0. It is no longer enough for controls to communicate with sensors and actuators. Today, real-time communication is also required between industrial plants and machines as well as their incoming and outgoing systems, a demand being met by real-time-capable virtualized fog servers with redundant design for high availability.

Microcontrollers (MCUs) are simple, programmable, and fully integrated systems typically used for embedded control. In addition to a processor (CPU), they generally include all of the memory and peripheral interfaces necessary on a single chip. This simplicity and integration means that MCUs are relatively inexpensive and generally consume very little power. Many popular hardware development platforms, such as Arduino, are built on top of MCUs.

MCUs generally do not have the resources (such as a Memory Management Unit) to run a higher-level operating system like Linux or Android.

MCUs are very effective in real-time applications where timing is critical. Production MCUs often run some flavor of a real-time operating system (RTOS)

Go to the profile of Dave Smith
Dave Smith
Android+Embedded. Developer Advocate, IoT @ Google.
Feb 9
Computing at the Edge of IoT
Over the past year, we’ve had some great conversations with developers about building IoT devices with the Android Things platform. A common question that comes up is whether the platform is suitable for the Internet of Things (IoT) given that the hardware is much more powerful than the microcontrollers typically found in the space today. To answer that question, let’s examine how hardware choice and use case requirements factor into different IoT system architectures.

“Computer programmer’s single microchip” by Brian Kostiuk on Unsplash
You already lost me, what’s a microcontroller?
Microcontrollers (MCUs) are simple, programmable, and fully integrated systems typically used for embedded control. In addition to a processor (CPU), they generally include all of the memory and peripheral interfaces necessary on a single chip. This simplicity and integration means that MCUs are relatively inexpensive and generally consume very little power. Many popular hardware development platforms, such as Arduino, are built on top of MCUs.

MCUs generally do not have the resources (such as a Memory Management Unit) to run a higher-level operating system like Linux or Android. Nor can they interface with high-speed peripherals like high-resolution cameras and displays. However, because the application code runs much closer “to the metal”, MCUs are very effective in real-time applications where timing is critical. Production MCUs often run some flavor of a real-time operating system (RTOS) to ensure tasks run in the exact amount of time required to ensure precise measurement and control.

All of these characteristics start to define applications where MCUs are a perfect fit…and where they aren’t.

The race to the cloud
Systems focused primarily (or entirely) on MCU hardware are based on what I’ll call the cloud first architecture. In this architecture, every edge device is connected directly to the internet (usually through WiFi), then provisioned and managed through a cloud service such as Google’s Cloud IoT Core. Inexpensive MCU platforms with built-in WiFi stacks, like the popular ESP8266, make designing systems like this very attractive.

In these systems, complex data analysis and decision making tasks are handled in the cloud back-end, while the device nodes perform data collection tasks or respond to remote control commands.

Overall, this is a nice balance. Hardware is inexpensive to replace and can run on small batteries for multiple years, and heavy compute resources are provided by cloud services that are easy to scale up

MCU hardware and a cloud-first architecture perform well in applications where bandwidth and network latency are less of a concern. Data payloads are small and can be uploaded to the cloud in batches. Here are some examples:

Distributed sensor-based data collection
Mobile asset monitoring and tracking

Living on the edge
There is a trend in IoT systems moving towards edge computing, enabled by the smartphone economy driving down the cost (and power consumption) of more capable hardware.

Google Home device. While the Google Assistant functionality is cloud-driven, the hotword detection happens locally on the device.

This is also true in industrial automation systems where latency and reliability are critical. In these systems, one or more intermediate gateway devices act as an interface between local edge devices (which may be MCU-powered) and any cloud services.

If we remove the need to connect each device to a cloud service, we can substitute a more power-efficient local network transport

The advancements in both artificial intelligence (AI) and machine learning (ML) promise to have big effects on IoT systems in the coming years. The ability for ML algorithms to find patterns and make predictions based on data collected by devices will quickly become a necessary component to the success

It all comes down to evaluating what your system’s needs truly are, and selecting the right tools for the job.

As we develop increasingly sophisticated technologies like self-driving cars and industrial internet of things sensors, it’s going to require that we move computing to the edge. Essentially this means that instead of sending data to the cloud for processing, it needs to be done right on the device itself because even a little bit of latency is too much.

Intel announced a new chip today, called the Intel Xeon D-2100 processor, to help customers who want to move computing to the edge.