This post provides an overview of the Smart Cities Akraino blueprint as well as an overview of key features and implementations of PARSEC in Akraino’s Release 5.
Overview – Akraino Blueprint: Smart Cities
The purpose of the Smart Cities blueprint family is to provide an edge computing platform based on Arm SoC and improve deployment flexibility and security within the edge computing. High-level relationships between functional domains is shown in the figure below:
(For the full description of the Smart Cities Reference Architecture please refer to the Smart Cities Documents.)
Smart Cities in Akraino R5– Key features and implementations in Akraino Release 5:
The Smart Cities blueprint’s security components is PARSEC, first available in Akraino’s Release 5. . The following is a brief introduction to PARSEC.
Parsec is the Platform AbstRaction for SECurity, a new open-source initiative to provide a common API to secure services in a platform-agnostic way.
PARSEC aims to define a universal software standard for interacting with secure object storage and cryptography services, creating a common way to interface with functions that would traditionally have been accessed by more specialised APIs. Parsec establishes an ecosystem of developer-friendly libraries in a variety of popular programming languages. Each library is designed to be highly ergonomic and simple to consume. This growing ecosystem will put secure facilities at the fingertips of developers across a broad range of use cases in infrastructure computing, edge computing and the secure Internet of Things.
(For more information of PARSEC: https://parallaxsecond.github.io/parsec-book/)
Software Defined Camera (SDC) blueprint
Security Cameras are seeing increasing growth in many subsegments such as commercial surveillance cameras, consumer surveillance cameras and in many other devices such as dashcams, baby monitors and other IoT vision devices. The total surveillance market is expected to be around $44B in the year 2025 growing at a CAGR of 13%. The other exciting thing happening in this market, along with the increase in the unit shipments of surveillance cameras, is the increasing use of artificial intelligence and machine learning (AI/ML) in these cameras and IoT devices. It is estimated that there are already a billion cameras installed in the world and this number would reach 2 billion by the year 2024. However security cameras are very different from many other devices because once installed, they stay installed for more than 5+ years. So it is critical to ensure that these devices continue to provide enhanced functionality over time.
In today’s world, there are many technologies that are “Software defined”. This has been possible because of many advancements in technology – at the bottom of the stack, you have the Operating System, then the virtualization layer – which actually kicked off the original software defined compute with Virtual machines etc. Then came containers and then the orchestrators for the containers such as k8s, k3s – these technological advancements paved the path to define a software defined data center. In the datacenter, after software defined compute, software defined networking and software defined storage started to become the norm.
Now the time is prime to move software defined trends to the edge devices as well. This is really a transformation that has started across many segments such as Automotive – cars, trucks etc. We do see this trend applied to many other edge devices and one of them would be cameras or as we and many others call it, Smart Cameras. The idea here is that once you buy a camera, the hardware is advanced so that you will be receiving continuous software updates which can be neural network model updates catered to the specific data that you might have captured using the camera or other updates related to functionality and security of the device.
By designing future camera products with cloud native capabilities in mind, one will be able to scale camera and vision products to unprecedented levels. If all the applications are deployed using a service oriented architecture with containers, a device can be continuously updated. One of the key advantages of this architecture would be to enable new use case scenarios that one might not have envisioned in the past with a simple on demand service deployment post sale through an app store. A simple example can be to deploy a new and updated license plate recognition app a year after the purchase of cameras. At the same time one of the other key advantages with this architecture would be to enable continuous machine learning model updates based on the data pertinent to the camera installations i.e be able to use the data that it is capturing and use it to train the models. With this, the model would be relevant to the specific use-case thus increasing the value of the camera itself. All of this is possible due to enabling a service oriented architecture model using containers and thinking cloud native first.
The other important aspect that will really bring together the security camera and vision products is the adherence to common standards at the silicon and the platform level. LF Edge member company, Arm, has started an initiative called Project Cassini and Arm SystemReady® , and it’s a key component of this initiative. As part of Arm SystemReady®, Arm is working with many ecosystem partners (OEMs, ODMs, silicon vendors) to drive a standards approach in order to scale deployments by replacing custom solutions with standards-based solutions and becoming the platform of choice for future camera deployments.
To address and simplify these challenges, Arm is designing and offering the solution as an open source reference solution to be leveraged collaboratively for future software defined cameras based on established standards focused on security, ML, imaging and cloud-native. The basis of the reference solution is the SystemReady® certified platform. SystemReady® platforms implement UEFI, U-Boot and Trusted standard based Firmware.
- With the base software layers compliant to SystemReady®, standard Linux distros such as Fedora, OpenSUSE distros or Yocto can be run on these platforms.
- Then the container orchestration service layer can run containerized applications, by using k3S.
- The next software layer is ML and Computer vision libraries. Arm has supported ML functions through Arm-NN and the Arm Compute library so that users can develop their ML applications using standard open-source ML frameworks such as PyTorch, Tensorflow etc.
- Similarly, with computer vision, it is critical to support open standards such as Video 4 Linux, OpenCV and BLAS libraries.
- Both complementary and necessary, is security – this is where orchestration and security microservices such as PARSEC, an open-source platform abstraction layer for security along with PSA certification process are critical, so that end users build the confidence that the product will be secure when deployed in the field. These secure services are the foundation to support and enable containerized applications for inferencing, analytics and storage.
Learn more about Project CASSINI here: https://wiki.akraino.org/display/AK/Project+Cassini+-+IoT+and+Infrastructure+Edge+Blueprint+Family.