What is Edge Computing?
Edge computing is a technology that has gained significant attention in recent years to improve the performance and efficiency of various edge AI computing applications. At its core, iot edge computing involves a network of sensors and controllers that collect data and communicate with a main computer that often make up Scada systems or Supervisory control and data acquisition. The data is then processed by the main computer either through edge of IoT devices or local edge servers while being close to the source of the data. In return, this approach gives feedback in real-time and offers better benefits than cloud computing where data is transmitted over a central data center. In addition, there has been a growing demand for real-time data processing and real-time analytics with increased use of edge devices such as point-of-sale (POS) kiosks, Internet of Things (IoT) devices, medical and industrial computers, IoT gateways, smart cameras and sensors, and more – has reached new heights as edge computing becomes increasingly critical in enabling the data to be processed more efficiently and quickly.
Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, it is estimated that this figure will reach 75% - Gartner
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Edge computing works by requiring a network of devices to be connected as close together as possible to capture processed information so that the collected data is stored in edge servers or in the cloud. The common types of IoT devices used for edge computing are industrial sensors, computing machinery equipment, wearables, edge servers, and gateway devices. These cutting edge computers are equipped with high-performance computing power, rich storage capacity, and network capabilities. Depending on the desired outcome, the data can feed into machine learning systems and analytics to move data more securely and smoothly while operating in real-time. As a result, edge computing can bring more accessible resources closer to where it needs to be, and having the data stored on local edge devices will substantially reduce the wait time for the distributed edge cloud to finish processing the data.
What are the benefits of edge computing?
Edge computing offers several advantages over traditional computing architectures especially when generated data is closer to edge devices. Here are some of the key benefits:
1. Latency: Edge computing may produce zero latency for end users. The time to travel between two data points on a network is greatly reduced as computational data get closer to one another. This results in a faster system response rate for real-time applications such as healthcare monitoring, traffic management, smart retail, industrial automation systems, and more.
2. Enhanced network efficiency: Edge computing can help streamline the amount of data sent over the network to the cloud which will help in reducing network congestion and improving bandwidth efficiency.
3. Improved security and reliability: Data is processed locally, reducing data breaches by limiting the amount of data sent over the internet and further improving analysis and encryption of sensitive material. If there was a cloud network outage or connectivity issue, it will still enable local computing machines to continue processing data without interference. This is possible because data are stored in local servers and storage devices.
Edge Computing Use Cases
Edge computing brings more opportunities for big businesses to leverage their processing and storage capabilities. There are many use cases for edge computing solutions across multiple industries. Here are the top 5 examples:
1. Energy & Utilities: Energy and utility companies have complex and challenging systems in place. From a strict regulatory environment, they rely heavily on a centralized computing system to manage all of their operations. Edge computing can be a game changer in the way data is handled including remote monitoring, managing EV-charging stations, reducing energy storage consumptions, and increasing the reliability of cybersecurity and smart grid systems.
2. Manufacturing: Manufacturers can utilize predictive maintenance to analyze and detect changes in production lines before an electrical outage occurs. The data from sensors and devices can detect defects in products while it is being produced to allow for edge analytics in quality assurance. Edge computing can also be used to optimize supply chains and improve processes.
3. Transportation: Edge computing gateways enable real-time traffic management to analyze traffic patterns, and reduce traffic congestion, which will enhance traffic flow and allow for real-time decision-making.
4. Healthcare: Edge computing computer vision is being used in hospitals for a number of edge computing applications including remote patient monitoring, disease detection and prevention, and medical imaging.
5. Retail: Edge computing in retail allows for sensors, cameras, and beacons to collect data about customer behavior and enable insights in real-time to help retailers provide a better shopping experience for their end users. Other benefits include better customer engagement, product recommendations, and inventory management.
How to choose the right Edge Computing hardware
There are many factors to consider when choosing the right cutting edge computer for edge computing and it depends on the specific use case. Here are 5 steps to get you started.
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1. Edge computers cannot compromise on performance. Edge computing devices must have the right performance requirements in order to quickly and accurately execute tasks or workloads. One major component is deciding on the processor. Determine the brain or type of central processing unit (CPU) to configure your system because it will affect the number of workloads it can handle and the speed it takes to complete the tasks. In other words, the core processor contains units called cores that allow the computer to handle multiple applications and processes simultaneously to operate and improve overall performance. A slow CPU can cause your computing system to delay and lag in performance while a fast CPU will get the job done.
Low-powered processors such as Intel® Atom® and Intel® Celeron® processors are great entry-level edge computing devices and are designed for low-powered applications in digital signage, HMIs, and IoT gateways. Axiomtek offers cutting-edge computers powered by Nividia Jetson and is perfect for edge computing applications that demand high-performance AI processing because of their small size and energy-efficient design. These AI computing platforms ranges from fanless edge AI systems, fanless embedded systems, and din rail PCs that is equipped with robust Socket Intel® Core® i3, i5, i7, and i9 processors to handle challenging industrial workloads at the edge with ease. Our popular entry-level industrial PCs includes ICO300-83B, ICO330, and eBOX626A to high-computing options such as AIE900A-AO, eBOX710-521 and IPC950. The embedded PCs is cost-effective and speeds up the time to market for easy integration requiring little to no supervision.
For more complex industrial workloads, Iot edge devices can be equipped with performance accelerators for real-time analysis and decision-making. The add-ons will provide sufficient power to support various advanced workloads at the edge. These include GPUs, FPGAs, NVMe, Multi-core CPUs, and VPUs.
• GPUs accelerate artificial intelligence and machine learning for Edge computing solutions.
• FPGAs optimize embedded systems that can be reprogrammed to a desired application or specific purpose.
• NVMe provides immediate access to data for faster processing times since data is operated locally within the drive.
• Multicore CPUs allow multi-task capabilities with each core used to complete specific tasks at the same time.
• VPUs enable performance acceleration for machine vision algorithms.
2. Edge computers must be fanless and possess rugged characteristics. The rugged design will fit in a wide range of industrial environments and in unpredictable environments where extreme temperatures, shock and vibrations, and debris are naturally occurring. Second, it must be a fanless design so that external dust, debris, and sand cannot damage sensitive components. The fanless design will enable edge devices to withstand exposure to extremely wide temperature ranges from -40⁰C to 70⁰C. This also helps with it being cableless since fewer moving parts that are in the chassis to eliminate the possibility of fewer parts failing.
3. Edge computers must have sufficient storage and rich I/O. Adequate storage enables edge devices to perform the best ability to collect, analyze, and process data quickly. To switch to an optimized storage capacity for high-speed data storage, the industry standard is to use solid-state drives (SSDs). SSDs are more optimal because it is lightweight, low-powered, and do not cause vibration due to having no moving parts. Additional storage is optional depending on the core business operations.
Axiomtek offers rugged edge systems that come equipped with a wide range of I/O ports to enable faster connectivity both modern and outdated machinery. Some I/O extension ports include COM, Ethernet (RJ45/M12), serials, video outputs, and general-purpose I/O ports that are useful for other functioning devices. The edge system eBOX710-521 is engineered to handle the most intensive data transmission and comes in a compact form factor with up to 12x GbE/4c 10GbE ports for massive data transfer.
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4. Edge computers must be secured and have a wide power range. These edge computing devices are often deployed in different environments where it needs different power inputs so having a wide range of compatible options is ideal. On top of that, it must be secure because it often operates in environments that are exposed to cybersecurity and physical risks. As a result, IoT edge devices must be safe and secure to ensure reliable operations in harsh environments are protected against cyber threats and attacks that can compromise sensitive data.
5. Edge computers must accomodate a variety of frameworks and platforms. It is important for cutting edge computers to perform reliably and securely with the data generated. While it may not be necessary to send data to the cloud; offering the right runtime environment will enable users to run code on IoT devices at the edge of the network. There are several frameworks available but this article will focus on two widely-used for edge computing:
• AWS IoT Greengrass (Amazon Web Services) allow users to broaden edge computing hardware to process data through AWS Lambda functions or Docker containers to perform analytics locally without sending the data to the cloud. This framework supports machine learning and other sophisticated edge computing applications with tools like managing and monitoring edge devices at scale.
• Azure IoT Edge framework allows custom code to be package in containers so that it can be converted to lightweight data used to easily deployed and managed on edge systems. It supports a wide range of programming languages such as Node.js, Python, Java, .NET Core, C#, C, C+++, and more.
Axiomtek offers a unique selection of frameworks and platforms to choose from custom OS and BSP to a variety of software integrations.
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Bottom Line
The future outlook for edge computing is promising as more industries are leveraging edge computing in their business operations. Axiomtek provides market-ready cutting edge computer solutions and customization services that will transform your business. If you have any questions, feel free to contact or email us at solutions@axiomtek.com to speak to one of our Edge Computing Specialists.
