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 computing applications. At its core, iot edge computing involves the processing and analysis of data at the edge of IoT devices or local edge servers while being close to the source of the data. In return, this approach 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 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
How does Edge Computing work?
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 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 reduce latency for end users. The processing time for data required to travel between two points on a network is greatly reduced as computational data get closer to each other. This results in a faster system response rate and processing time for real-time applications such as healthcare monitoring, traffic management, smart retail, and industrial automation systems.
2. Increased network efficiency: IoT edge computing can help minimize the amount of data sent over the network to the cloud, 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 edge 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: These companies have very complex and challenging systems in place because they rely on a centralized computing system to manage their operations. Utilizing edge computing applications 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 in intelligent grid systems.
2. Manufacturing: Industrial edge computing use predictive maintenance to analyze and detect changes in production lines before an outage occurs. Data from sensors and devices can detect defects in products while it is being produced to allow for real-time quality control. Edge computing can also be used to manage inventory levels to optimize supply chains and improve efficiency.
3. Transportation: Edge computing gateways enables real-time traffic management to analyze traffic patterns, reduce congestion, enhance the flow of traffic, and have real-time decision-making.
4. Healthcare: Edge computing computer vision are 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 to enable real-time insights that will help retailers provide 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.
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. On contrary, Axiomtek offers cutting-edge computers 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 edge computing sytems ranges from ICO300-83B, ICO330, and eBOX626A to high-computing options such as eBOX710-521 and IPC950. The edge computers 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 ruggedized. Edge computers must 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 closed system will 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.
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.
The future outlook for edge computing is promising as more industries are utilizing and improving this technology. Axiomtek provides market-ready cutting edge computer solutions that will transform your business. If you have any questions, feel free to contact or email us at firstname.lastname@example.org to speak to one of our Edge Computing Specialists.