{"id":23390,"date":"2024-09-25T10:54:41","date_gmt":"2024-09-25T02:54:41","guid":{"rendered":"https:\/\/www.robustel.com\/?p=23390"},"modified":"2024-10-25T16:59:26","modified_gmt":"2024-10-25T08:59:26","slug":"edge-computing-what-you-need-to-know","status":"publish","type":"post","link":"https:\/\/www.robustel.com\/au\/iot-technology-solutions-blog\/edge-computing-what-you-need-to-know\/","title":{"rendered":"Edge Computing: What it is and Why Does It Matter?"},"content":{"rendered":"\n\n\t<p>Beyond common cloud computing, <a href=\"https:\/\/www.robustel.com\/iot-technology-solutions-blog\/the-rise-of-edge-computing-what-you-need-to-know\/\" target=\"_blank\" rel=\"noopener\">edge computing is also becoming a game-changer in the IoT world<\/a>. Reduced latency, real-time responsiveness, and increased bandwidth economy are just a few benefits that come with bringing data processing and storage closer to the point of generation.<\/p>\n<p>&nbsp;<\/p>\n<p>However, let us explore edge computing even more. In comparison to cloud computing, <strong>what are its benefits? Which uses is edge computing useful for?<\/strong><\/p>\n<p>&nbsp;<\/p>\n<h2>What is Edge Computing?<\/h2>\n<p>&nbsp;<\/p>\n<p>Although cloud computing has probably received a lot of attention, edge computing is also a important player. While local computing processes data directly on your device and cloud computing processes data in distant data centers, edge computing lies in the middle and solves issues that neither can handle on its own:<\/p>\n<ul>\n<li><strong>Reduced Latency.<\/strong> Edge computing processes data in real-time or near real-time, decreasing the latency of transferring data to the cloud and back. This is perfect for applications such as <a href=\"https:\/\/www.robustel.com\/category\/industrial-automation\/\" target=\"_blank\" rel=\"noopener\">industrial automation<\/a> or driverless cars that need to react quickly.<\/li>\n<li><strong>Reduced Network Burden.<\/strong> Less data needs to be transferred to the cloud, freeing up network capacity, when data processing duties are handled closer to the source.<\/li>\n<li><strong>Cost Savings.<\/strong> Over time, decreased bandwidth costs are associated with less data transfer.<\/li>\n<li><strong>Enhanced Data Privacy and Security.<\/strong> By processing sensitive data locally or on edge devices, the chance of data exposure occurring during transmission or cloud storage is decreased.<\/li>\n<li><strong>Offline Capabilities.<\/strong> Even when your device is not connected to the internet, edge computing enables some level of data processing and calculation.<\/li>\n<\/ul>\n<p>Edge computing essentially allows for faster, more effective, and more secure data processing by bringing computation and data storage closer to the point of demand.<\/p>\n<p>&nbsp;<\/p>\n<h2>Edge Computing Architecture<\/h2>\n<p>A typical edge computing setup involves a three-tiered structure:<\/p>\n<ul>\n<li><strong>Cloud Layer<\/strong> is the well-known area of distant data centers in charge of processing large amounts of data and storing it for an extended period of time. This is the location where a lot of complicated analytics, machine learning, and data preservation happen.<\/li>\n<li><strong>The Edge Layer:<\/strong> The magic of near real-time processing takes place at this layer. Edge servers or gateways that are situated in local networks or on-premises, nearer to the data sources, are included. In order to make choices quickly and minimize the need to transfer everything to the cloud, the edge layer filters, aggregates, and processes data locally.<\/li>\n<li><strong>The Device Layer:<\/strong> At the network&#8217;s edge, this layer includes the wide array of linked devices and sensors. These gadgets produce unprocessed data, such as CCTV camera pictures and temperature measurements from smart factories. These devices will collect data and forward it to the edge layer for additional analysis, though they may also perform some basic processing.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>Edge Computing Classifications Based on Processor Type<\/h2>\n<p>Edge computing can be broadly categorized into two types based on the processor used:<\/p>\n<ul>\n<li><strong>Edge CPU.<\/strong> Central Processing Units (CPUs) handle the majority of general-purpose computing tasks. Among these are employment in data processing, network management, and light-weight AI inference. The primary objective of CPU design is to strike a compromise between low power consumption and general-purpose computational power.<\/li>\n<li><strong>Edge GPU.<\/strong> High-level parallel computing workloads are a strength of Graphics Processing Units (GPUs). These tasks involve dealing with large-scale models, processing graphics, and training and inference in deep learning models. GPUs are used extensively in applications that demand large amounts of computational power because of their superiority in parallel processing.<\/li>\n<\/ul>\n\n<h2>Where does the data used for computing originate?<\/h2>\n<p>&nbsp;<\/p>\n<p>With the rise of the <a href=\"https:\/\/www.robustel.com\/iot-technology-solutions-blog\/what-is-iot\/\" target=\"_blank\" rel=\"noopener\">Internet of Things (IoT)<\/a>, machines and equipment are becoming increasingly intelligent. Significant computer power and an immense amount of data are need for this intelligence. In this situation, computing is the queen that controls how the data is used, while data is the king.<\/p>\n<p>&nbsp;<\/p>\n<p>More data is needed for machine and equipment intelligence. This is where it originates:<\/p>\n<ul>\n<li>Operational Technology (OT) Data: This includes information gathered from pertinent sensors on the equipment as well as data produced by the machines themselves, such as operational status and malfunction information.<\/li>\n<li>Information Technology (IT) Data: This provides a more comprehensive context than just machine activities and includes data gathered from other business systems.<\/li>\n<\/ul>\n<p>Through the integration, examination, and comprehension of this IT and OT data, we may create intelligent applications. Applications that increase the machine&#8217;s intelligence, boost quality control while the equipment is operating, or allow predictive maintenance to avert unplanned malfunctions are a few examples of these.<\/p>\n<p>&nbsp;<\/p>\n<p>Connectivity is the cornerstone of it all. The ability to connect and communicate is necessary for all of this data. Thus, the first and most important step in allowing machine intelligence and realizing the full potential of the Internet of Things is to create dependable connections.<\/p>\n<p>&nbsp;<\/p>\n<h2>What scenarios require the use of edge computing?<\/h2>\n<p>&nbsp;<\/p>\n<p>Edge computing is ideal in scenarios where:<\/p>\n<ul>\n<li><strong>Low Latency is Critical.<\/strong> The reduced latency edge computing provides is advantageous for applications like industrial automation, autonomous vehicles, and remote surgery that require real-time or almost real-time data processing and response. It just isn&#8217;t quick enough to send data to a distant cloud and wait for a response.<\/li>\n<li><strong>Bandwidth is Limited or Expensive:<\/strong> Edge computing enables data processing and analysis closer to the source, minimizing the need to send everything to the cloud, if transporting huge amounts of data to the cloud is difficult owing to bandwidth limits or expense.<\/li>\n<li><strong>Data Locality Matters.<\/strong> Edge computing allows local processing and storage in scenarios where data must remain within a certain geographic location or on-premises due to privacy, security, or compliance restrictions.<\/li>\n<li><strong>Connectivity is Unreliable:<\/strong> Edge computing offers some degree of autonomy when programs must continue to run even with sporadic or unstable network connections to the cloud. When connectivity is restored, data can be synchronized later and critical functions can continue locally.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>What Functions does edge computing need to realize in the Industrial Internet of Things (IIoT) system?<\/h2>\n<p>&nbsp;<\/p>\n<p>The IIoT architecture can be summarized as <strong><em>&#8220;end-pipe-edge-application.&#8221;<\/em><\/strong> To achieve its goals, edge computing in the IIoT needs to realize the following core functions:<\/p>\n<ul>\n<li><strong>Southbound Data Acquisition (End)<\/strong>. This involves collecting information from diverse devices, machinery, and sensors. For edge computing solutions to work with a variety of devices, they must support a large number of industrial protocols.<\/li>\n<li><strong>Northbound Communication (Pipe).<\/strong> Data must be securely transferred to on-site or cloud servers after collection. Depending on the needs of the application, edge computing allows data to be routed from several sources to distinct destinations.<\/li>\n<li><strong>Computing Engine (Edge).<\/strong> Edge computing does more than merely store and communicate data; it also processes and analyzes it locally. In order to improve operational efficiency and troubleshooting, this entails supporting a variety of databases, putting data analysis algorithms into practice, and offering data visualization capabilities.<\/li>\n<li><strong>Development Engine (Application).<\/strong> Applications for IIoT varies significantly throughout sectors and use cases. The creation of unique applications is made easier by an open computing platform equipped with features like object-oriented programming, graphical programming, and high-level languages.<\/li>\n<\/ul>\n<p>Support for containerization considerably simplifies the deployment of applications across many platforms.<\/p>\n<ul>\n<li><strong>Security Engine.<\/strong> As IIoT use increases, it is critical to guarantee strong security. A thorough security system ought to include:\n<ul>\n<li><strong>Security for Operations.<\/strong> Implement robust security procedures that adhere to ISO 27001 and IEC 62443-4-1 requirements.<\/li>\n<li><strong>Multiple Layers of Defense.<\/strong> Put security measures in place for the network, hardware, system, and operations.<\/li>\n<li><strong>Frequent Evaluation of Penetration.<\/strong> Penetration testing should be done on a regular basis to find vulnerabilities and fix them early.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Companies like <a href=\"https:\/\/www.robustel.com\/\" target=\"_blank\" rel=\"noopener\">Robustel<\/a> have recognized the importance of security and built comprehensive solutions to address these concerns.<\/p>\n<p>&nbsp;<\/p>\n<h2>Service Models for Industrial Edge Computing<\/h2>\n<p>&nbsp;<\/p>\n<p>From the standpoint of product distribution, industrial edge computing provides the following service models:<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Gateway Hardware Only.<\/strong> Although it doesn&#8217;t have an operating system or middleware, it nonetheless offers the basic connection and communication features. Ideal in situations when minimal data transmission is all that is needed.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Gateway + OS.<\/strong> By integrating an operating system on top of the gateway, this model offers enhanced data processing and management capabilities, enabling more complex tasks to be performed at the edge.<\/p>\n<p>&nbsp;<\/p>\n<p><strong><a href=\"https:\/\/www.robustel.com\/product_cat\/industrial-edge-computing-gateway\/\">Gateway<\/a> + OS + Middleware (Tools).<\/strong> This model further integrates middleware and development tools, making it easier for customers to build and deploy their applications on the edge platform.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Gateway + OS + Middleware (Tools) + Application.<\/strong> A comprehensive, one-stop solution encompassing hardware, software, middleware, and a pre-built application. It addresses specific customer needs directly, requiring minimal additional development.<\/p>\n<p>&nbsp;<\/p>\n<p>These service models offer flexibility, allowing users to select the appropriate level of service that aligns with their specific requirements and budget.<\/p>\n<p>&nbsp;<\/p>\n<h2>Edge Computing Application Scenarios<\/h2>\n<p>&nbsp;<\/p>\n<p>Industrial edge computing has found widespread adoption across various industries. Some of the most common applications include:<\/p>\n<ul>\n<li><strong>Industrial Manufacturing.<\/strong> Edge computing nodes deployed on the factory floor enable real-time monitoring and intelligent adjustments to the production process. This leads to improved product quality and enhanced production efficiency.<\/li>\n<li><strong>Smart Buildings.<\/strong> Edge computing processes data from various sensors within buildings to optimize energy usage, improve management practices, and create a more comfortable and productive living and working environment.<\/li>\n<li><strong>Power and New Energy.<\/strong> In power and new energy systems, edge computing facilitates real-time monitoring and predictive analysis of power grids and energy supply conditions, contributing to improved stability and security within the power infrastructure.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>Cloud vs. Edge: Deciding Where to Process<\/h2>\n<p>&nbsp;<\/p>\n<p><strong>Edge Computing:<\/strong><\/p>\n<ul>\n<li><strong>Large Data Volumes + Real-time Needs.<\/strong> Applications that generate substantial amounts of data and require immediate insights (e.g., quality analysis, process optimization, predictive maintenance) are well-suited for edge computing. The reduced latency of processing data locally at the edge enables faster decision-making and responses.<\/li>\n<li><strong>Industry-Specific Computing.<\/strong> When computational requirements are closely linked to a particular industry or sector, and data locality is crucial due to regulations or privacy concerns, edge computing can be advantageous. Examples include factory floor analytics, where real-time data processing drives immediate actions, or oil rig monitoring in remote locations with limited connectivity.<\/li>\n<\/ul>\n<p><strong>Cloud Computing:<\/strong><\/p>\n<ul>\n<li><strong>Standardized Tasks.<\/strong> Applications with well-defined workflows and standardized computation needs can often leverage cloud resources effectively. The cloud&#8217;s scalability and vast computing power make it ideal for handling large-scale data processing and analysis tasks that don&#8217;t require immediate, real-time responses.<\/li>\n<li><strong>Lightweight Applications.<\/strong> <a href=\"https:\/\/www.robustel.com\/solution\/\" target=\"_blank\" rel=\"noopener\">Industrial IoT systems<\/a> with primarily basic monitoring and data collection requirements can be adequately managed by lightweight edge devices or gateways. These devices can perform initial data filtering and aggregation, while more complex analysis or long-term data storage can occur in the cloud. This hybrid approach optimizes resource utilization and cost-efficiency.<\/li>\n<\/ul>\n\n<h2>How Edge Computing Synergizes with Cloud Computing<\/h2>\n<p>&nbsp;<\/p>\n<p>In the pursuit of machine and equipment intelligence, edge computing and cloud computing will increasingly collaborate to achieve greater efficiency and capabilities. This synergy manifests in several ways:<\/p>\n<ul>\n<li><strong>Division of Labor:<\/strong> Edge computing handles real-time data processing and preliminary analysis directly on or near the equipment, reducing latency and bandwidth requirements. Critical information or data requiring more extensive processing is then sent to the cloud. The cloud, with its vast computing resources and storage capacity, performs in-depth data mining, analysis, and machine learning model training.<\/li>\n<li><strong>Cloud-Side Collaboration:<\/strong> This model leverages both the real-time capabilities of edge computing and the powerful data processing and storage capabilities of the cloud. It enables efficient data handling at the edge while utilizing the cloud for advanced analytics and insights.<\/li>\n<li><strong>Intelligent Optimization:<\/strong> AI algorithms and models are trained in the cloud using historical and aggregated data. These trained models are then deployed to edge devices for inference, enabling autonomous decision-making and intelligent control directly at the source. This approach enhances equipment intelligence and operational efficiency.<\/li>\n<\/ul>\n<p>For instance, in smart factories, edge computing nodes equipped with AI algorithms are deployed on machines and equipment along the production line. These nodes enable real-time monitoring and intelligent adjustment of the production process, leading to improved product quality, increased efficiency, and reduced downtime. The edge handles immediate decision-making, while the cloud provides centralized data storage, advanced analytics, and model updates.<\/p>\n<p>&nbsp;<\/p>\n<h2>The Bottom Line<\/h2>\n<p>&nbsp;<\/p>\n<p>In summary, connectivity, computing, and data are the three core elements driving machine and device intelligence.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Connectivity<\/strong> lays the foundation for <strong>data<\/strong> acquisition, data serves as the fuel for generating insights, and <strong>computing<\/strong> is the key to transforming that data into actionable intelligence.<\/p>\n<p>&nbsp;<\/p>\n<p>Looking ahead, with the ongoing advancements in IoT, AI, and other technologies, coupled with the expanding range of application scenarios, <strong>edge computing<\/strong> will assume an increasingly crucial role in achieving machine and equipment intelligence. It will further propel the deep integration of the digital economy and the real economy, driving innovative development and transforming industries.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Beyond common cloud computing, edge computing is also becoming a game-changer in the IoT world. Reduced latency, real-time responsiveness, and increased bandwidth economy are just a few benefits that come with bringing data processing and storage closer to the point of generation. &nbsp; However, let us explore edge computing even more. In comparison to cloud [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":23403,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[],"class_list":["post-23390","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-iot-technology-solutions-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Edge Computing: What it is and Why Does It Matter? | Robustel<\/title>\n<meta name=\"description\" content=\"Edge computing brings data processing closer to the source for faster insights, reduced latency, and improved bandwidth efficiency. 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