The SkyLab MEC Platform Makes It Easy to Deploy Intelligent Edge AI for Road Traffic Solutions

Experts estimate that road traffic volume will double by 2040 as rising populations generate more congestion and pedestrian density.1 This will lead to an exponential rise in costs associated with managing traffic, maintaining roads, and supporting transportation-related technology to keep cities moving efficiently. Projections show the cost of traffic congestion will balloon to over USD 1 trillion,1 creating a massive financial burden. In addition to cost, current traffic conditions cause 50 million injuries to drivers and pedestrians alike,1 posing serious challenges to quality of life. Traffic controls of previous generations have relied on discrete solutions and legacy infrastructure that is siloed, expensive, and aging. To tackle the modern challenges of a hyperconnected world and rapid urbanization, cities are looking to invest in smart, connected, and efficient traffic management solutions.

Challenge: The cost and complexity of AIoT Cities have been investing in smarter traffic management solutions for decades, but over time, disparate technologies and hardware have become costly and difficult to manage. Cities need solutions that converge edge computing capabilities and workloads across networks to drive greater data insights, better user experiences and services, and cost efficiencies. By abstracting and consolidating resources into a converged solution, cities can simplify their infrastructure with low-latency 5G connectivity and fast edge AI performance. The barriers to entry include expensive hardware, high risk in migrating to new infrastructure, and the training required to deploy these systems. For example, a statewide connected-vehicle application was recently projected to cost USD 6.6 million over two years to install roadside units at 1,600 intersections.2 Total costs included USD 2 million for roadside unit hardware, USD 1.4 million for deployment, USD 3.2 million for configuration and support, and an additional USD 1 million for onboard units.2

“In traffic use cases, you have large amounts of video data being generated in many scenarios, many locations at the same time. SkyLab MEC, developed in collaboration with Intel and Supermicro, optimizes and processes high volumes of data at the edge, with real-time data processing at low latency. This allows the use of AI to help optimize the flow of traffic, reduce congestion and environmental pollution, and improve quality of life for people in urban areas.” — Stephen Ho, Group Chief Operating Officer

at SkyLab

SkyLab and Supermicro use the Intel® Edge Software Hub to overcome complexity and reduce time to market

Solution BriefConverged Edge AIIntelligent Traffic Management

Solution Brief | Intelligent Traffic Management

Solution: Converged edge smart traffic management A converged edge solution combines networking workloads with analytics, media, inferencing, and any other type of IoT workload and makes them deployable across any location on the network. The SkyLab Multi-access Edge Computing (MEC) platform is a converged edge solution that offers flexible configurations developed on Supermicro hardware, built on Intel® technologies, and uses resources from the Intel® Edge Software Hub to enhance scalability. This helps city transportation agencies merge diverse workloads of network, camera sensors, and inference on a common

infrastructure to help reduce complexity and increase utilization and manageability. The SkyLab MEC platform can be tailored to meet the specific needs of traffic management and has demonstrated success in use cases such as smart refueling stations, smart solar panel metering, and smart public rail systems. SkyLab and Supermicro help cities develop and deploy the right traffic management solution that can reduce congestion and emissions and integrate the latest technology into legacy environments.

How it worksThe SkyLab MEC platform is built for core-to-edge deployments, using Kubernetes containerization to abstract and aggregate physical resources at the edge. The platform includes an orchestration layer that provides top-down remote visibility and access to edge appliances, so administrators can easily manage the system, push

updates, or deploy more software apps from the SkyLab marketplace. The SkyLab MEC platform is built on Supermicro hardware, which is ruggedized to withstand harsh outdoor environments such as heat, rain, and humidity.

Figure 1. The SkyLab MEC platform interfaces with sensors, processes data at the edge, and connects to cloud infrastructure for easy management and admin control.

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Solution Brief | Intelligent Traffic Management

Components of the converged edge solutionTraditional traffic management solutions rely on discrete and expensive enterprise-grade GPUs to process images. The SkyLab MEC platform allows high volumes of data to be processed at the edge—and includes AI vision graphics processing for object recognition and inference—without the need to backhaul data to centralized networks or the cloud. This helps reduce network usage, bandwidth requirements, and infrastructure complexity. The platform leverages specific microservices from OpenNESS and adopts optimal configurations from Intel’s Converged Edge Reference Architecture (CERA) and the Intel Edge Software Hub to achieve optimal performance on Intel® hardware. Intel® Xeon® processors, Intel® Movidius™ Myriad™ VPUs, and the Intel® Distribution of OpenVINO™ toolkit also help meet high-level performance and efficiency goals.

Typical MEC configuration for use in traffic management:

• Intel® Xeon® processors

• Intel® Movidius™ Myriad™ X VPUs

• 32 GB DDR4-2666 Mhz SO-DIMM

• 960 GB Intel® SSD S4510

The Intel Distribution of OpenVINO toolkit

During platform development, SkyLab used the Intel Distribution of OpenVINO toolkit to help tune video analytics workloads for the embedded Intel® processor and VPU. With preoptimized libraries of functions and kernels, the toolkit helps developers code, train, and deploy deep learning inference, computer vision, and hardware acceleration models in heterogeneous environments. According to Stephen Ho, Group Chief Operating Officer at SkyLab, “I’ve seen people on the team who were not exposed to video analytics before, but they picked it up quickly and were able to deploy.” The toolkit was easy to learn and use, enabling SkyLab’s teams to overcome a gradual learning curve and get the modules working faster.

In a similar real-world use case, a deployment optimized with the Intel Distribution of OpenVINO toolkit was able to achieve greater than 90 percent accuracy for license plate recognition of opted-in gas station customers.3 As a result, the MEC was able to track and digitize customer behavior for an operator with over 5,000 locations, and the customer deployed the solution in just four weeks.

Open Network Edge Services Software (OpenNESS) toolkit

The SkyLab MEC solution used OpenNESS OVS-DPDK and SRIO microservices to improve edge application data plane performance. This allows customers to streamline the integration of new microservices across the converged edge network. Edge devices with OpenNESS will automatically authenticate themselves with the cloud network while cloud servers automatically discover and connect to edge devices, helping reduce setup time. The toolkit also includes a web-based GUI so developers can easily deploy a new application.

Supermicro hardware based on CERA

Supermicro supports the SkyLab MEC platform with a hardware solution they built based on CERA, a reference architecture that converges IoT hardware and software with wireless network infrastructure to achieve workload convergence. The result is an all-in-one solution that provides low latency, high bandwidth, and dense networking to support advanced, compute-heavy workloads like AI vision at the edge. Supermicro systems based on CERA are autonomous and self-managing, empowering both solution providers and customers to enjoy the benefit of low-touch deployments at scale.

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Solution Brief | Intelligent Traffic Management

Holistic platform drives fast go to market The SkyLab MEC platform is scalable and offers many interface options, so it can easily integrate with other systems over a 4G/LTE or 5G connection. The platform is also designed for horizontal use cases that are both hardware and industry agnostic. This versatility contributes to the platform’s ability to be deployed virtually anywhere, while the MEC can be configured for any number of edge applications.

Stephen Ho recounts that a typical deployment can be up and running within a month, once a deployment site is available for installation, as seen in a use case in Singapore. After deployment, projects can move quickly into calibration for each specific environment, adjusting for lighting and weather conditions. Technicians can also deploy, configure, and monitor new services remotely, reducing the need for on-site inspections or security checks.

Easy accessibility as an Intel® IoT RFP Ready Kit The SkyLab MEC platform is currently available and easily accessible in the form of an Intel IoT RFP Ready Kit, with Supermicro hardware as a configuration flavor. Intel RFP Ready Kits are validated hardware-software stacks that solution vendors can add to their portfolio of offerings. Backed by Intel expertise and IoT industry leadership, these kits save time and help customers deploy quickly and experience benefits right away.

Accelerating development with the Intel Edge Software Hub SkyLab and Supermicro use the Wireless Network-Ready Intelligent Traffic Management reference implementation on the Intel Edge Software Hub to reduce time to market and accelerate deployments. This and other reference implementations make enabling use cases, such as anonymized pedestrian counting and vehicle recognition, more accessible to developers using the SkyLab MEC platform. Hardware configurations of optimized components are available to aid in selection and expedite commercialization.

Intelligent traffic management that’s easy to deploy The SkyLab MEC delivers smart traffic monitoring and management to empower cities to overcome the challenges of congestion, population density, and urban growth. As an Intel RFP Ready Kit, and combined with resources from the Intel Edge Software Hub, the MEC solution and Supermicro hardware can deploy quickly and start having an impact on quality of life right away. The easy accessibility of developer resources and toolkits will also encourage creativity and innovation in future edge AI solutions, paving the way to a brighter future.

TRAFFIC ANALYTICS• Differentiate types of vehicle or pedestrian traffic

• Adjust signal length to help reduce congestion

ENVIRONMENTAL SENSORS• Collect temperature and air quality data

• Improve situational awareness

AUTONOMOUS VEHICLES• Maintain line-of-sight connection

• Improve coordination between vehicles and help avoid collisions

Figure 2. The Supermicro hardware solution, based on Converged Edge Reference Architecture (CERA), supports compute-heavy edge AI use cases, including traffic analytics.

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Solution Brief | Intelligent Traffic Management

Learn more

Intel Edge Software HubThe Intel Edge Software Hub provides quick access to a growing list of containerized, vertical-specific software packages and toolkits to help developers rapidly develop edge solutions.

Learn more ›

Smart Road Infrastructure

With smart road technology, a safer and more-sustainable future of transportation is right around the corner.

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SkyLab MEC

Designed for edge deployments, SkyLab MECs deliver the latest in containerization technology to accelerate applications and offload processing and network usage from the cloud.

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Supermicro 5G

Embedded IoT from Supermicro drives optimization for 5G virtual and container hybrid services, bringing low-latency AI to customer premises solutions.

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About SkyLabSkyLab is a Singapore-based organization that is actively looking for partners in the APAC region who are interested in developing and building 5G IoT solutions. Email SkyLab to get started.

skylabteam.com

About SupermicroSupermicro is a global innovator in high-performance server, cloud, enterprise IT, and embedded solutions and offers a broad range of solutions to drive customer success.

supermicro.com

1. “Paving the Way Forward,” May 2020. https://www.intel.com/content/www/us/en/internet-of-things/transportation-road-infrastructure-ebook.html.2. “A Signal Phase and Timing (SPaT) system for connected vehicle applications in Georgia was estimated to cost $6.64 million (2019–2020).” Intelligent Transportation Systems Joint Program Office website, March 2020. https://www.itskrs.its.dot.gov/its/benecost.nsf/ID/087bb15032f3188d85258519006723dd.3. Source: Internal SkyLab performance data. Intel does not control or audit third-party data. You should review this content, consult other sources, and confirm whether referenced data are accurate.

Notices and disclaimersIntel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel’s Global Human Rights Principles. Intel® products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.Intel® technologies may require enabled hardware, software, or service activation.No product or component can be absolutely secure. Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.Your costs and results may vary.© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. 0321/SS/CMD/PDF

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