Edge computing is a new-age technology that originated in the 1990s. Akamai - a technology-based company, introduced a content delivery network(CDN). These nodes were presented at specific locations to make it nearer for the user to find the cache memory content of images, docs, etc.
With the help of approaches like lean manufacturing, companies such as Toyota and Six Sigma made manufacturing prominent while trailing potency benefits in the 1980s and 1990s. In contrast, the competence of industrial companies faced a slow downfall.
Industrial organizations had to consider progressive policy and procedure to upgrade formulation, consummation, and benefit. Edge computing technology aids different devices in remote areas in processing the data toward the network's edge.
Edge Computing vs Cloud Computing: A Brief Overview
Cloud Computing refers to using remote servers, typically assessed over the internet, to store, manage, and process data. In cloud computing, the resources are centralized and commonly owned and operated by a third-party provider. Users can assess these resources on demand, typically paying only for the aid they use.
Several applications use cloud computing, from running virtual machines and hosting websites to processing big data and running machine learning algorithms.
Edge Computing - On the other hand, edge computing involves processing data closer to the source of that data, typically on the devices or machines generating the data.
Edge computing applications can provide faster response time and reduces the need to transmit a large amount of data over a network, which can be especially important for applications that require real-time processing, such as IoT devices or autonomous vehicles.
In Edge computing vs cloud computing, cloud computing is about decentralizing computing resources and assessing them remotely. Meanwhile, edge computing is about decentralized computing resources and bringing them closer to the source of data generation. Both approaches have advantages and disadvantages; choosing between them depends on the specific requirements of each application.
Top 5 Essential Applications of Edge Computing
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Internet of Things - Edge computing is essential for IoT because it allows processing data closer to the source, reducing latency and improving response times. It can be crucial for time-based applications like autonomous vehicles, smart factories, and remote monitoring systems.
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Video Surveillance - Edge computing can help to process video data in real time, for a central server analysis reduces the need to transmit a vast amount of data. It can be helpful in applications such as video surveillance. It requires real-time video data analysis for security purposes.
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Healthcare - There is demand for Edge computing in healthcare applications to process and analyze data from medical devices, such as wearable devices or implantable sensors. It can improve patient outcomes by providing real-time feedback to healthcare providers and enabling more personalized treatment plans.
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Energy - Businesses use edge computing in energy applications to optimize renewable energy sources, such as solar or wind energy. By processing data in real time, edge computing technology can help to manage the variability of renewable energy sources and reduce the need for costly energy storage solutions.
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Retail - Edge computing can be used in retail applications to process data from in-store sensors and devices, such as beacons, cameras, and RFID tags. It can improve customer experiences by enabling real-time recommendations, personalized offers, and targeted marketing.
How does Edge Computing work? An Explanation with Examples
Edge computing applications bring data processing and services closer to the network's edge, closer to where the data is generated or consumed. It reduces the need for data transmission to a central data center or cloud for processing, analysis, and storage.
The following are IoT Edge Computing Examples of how it works -
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Smart Homes: Smart homes are an excellent example of edge computing. Smart home devices, such as thermostats, lights, and security cameras, generate data that needs to be processed quickly. With edge computing technology, the data is processed on the device or in a nearby gateway, reducing the need to send the data to a cloud data center.
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Autonomous vehicles: Autonomous vehicles generate large amounts of data, such as lidar and camera data, that must process in real time for safe and reliable operation. In edge computing applications, the data can be processed on the vehicle or in nearby edge data centers, allowing faster response times and reducing the need for data to be sent to a remote cloud data center.
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Smart Factories: Smart factories generate vast amounts of data from sensors, machines, and other devices that must be processed and analyzed in real-time to optimize production processes. Edge computing allows this data to be processed on-site, enabling faster diagnosis and treatment decision-making and reducing the need to send data to a remote cloud center.
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Telemedicine: Telemedicine applications generate accurate data from medical devices that must be processed and analyzed quickly. Edge computing technology allows this data to be processed locally, enabling faster diagnosis and treatment decisions.
In conclusion, edge computing applications work by processing data closer to the source of data generation, reducing the need for data transmission to a central data center or cloud for processing.
Edge Computing Software
Edge computing is the custom of refining data near the contour of the network where data can accomplish it than in a concentered data processing repository. As per the segment of GE's digital conversion voyage, it gave rise to the Predix platform. Predix Platform is the podium of applications and services originated for the better functioning of industries. It majorly spotlights the factors such as cost-cutting and enhancing the productivity levels of the sectors.
Predix machine concedes industries to keep a check on network edge appliances all the time. It approves industrials to carry visibility, supervision, and analytic vision to significant groundwork elements.
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