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edge computing

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. The “edge” in edge computing refers to the geographical distribution of computing resources at the edge of the network, closer to devices or data sources. This approach addresses the limitations of traditional cloud computing models in scenarios requiring real-time processing, reduced latency, and localized decision-making.


Edge computing has a wide range of applications across various industries:


  1. Manufacturing: Real-time analytics and machine learning at the edge can detect production errors and improve manufacturing processes[1].
  2. Healthcare: Edge devices can monitor critical patient functions and store data locally, improving privacy protection[3].
  3. Energy: Companies use edge computing to collect and store data on oil rigs, gas fields, wind turbines, and solar farms[3].
  4. Retail: Retailers can personalize shopping experiences and rapidly communicate specialized offers[4].
  5. Autonomous Vehicles: These vehicles require real-time data processing for instant response and cannot rely on a remote server for split-second decisions[3].


There are many benefits to edge computing, including:


  1. Reduced Latency: By processing data near the source of data generation, edge computing reduces the latency involved in sending data to a centralized data center or cloud for processing. This is crucial for real-time applications, such as autonomous vehicles, industrial automation, and real-time analytics.
  2. Bandwidth Savings: Transmitting large volumes of data over a network can be bandwidth-intensive and costly. Edge computing alleviates this by analyzing and processing data locally, reducing the amount of data that needs to be sent over the network.
  3. Improved Privacy and Security: Processing data locally can enhance privacy and security, as sensitive information does not need to be transmitted over the internet to a centralized location. This is particularly important for industries handling sensitive data, such as healthcare and finance.
  4. Scalability: Edge computing allows organizations to scale their computing resources by adding more edge devices, rather than expanding centralized data centers. This can be more cost-effective and efficient, especially in distributed networks.
  5. Reliability: By decentralizing computing resources, edge computing can increase the reliability of data processing. In scenarios where network connectivity is intermittent or unreliable, edge computing ensures that local processing and decision-making can continue without interruption.
  6. Use Cases: Edge computing is used in various applications, including IoT devices, smart cities, content delivery networks, and more. It enables smart applications and devices to perform reliably, securely, and with low latency, enhancing user experience and operational efficiency.


Citations:

[1] https://www.techtarget.com/searchdatacenter/definition/edge-computing

[2] https://www.accenture.com/us-en/insights/cloud/edge-computing-index

[3] https://aws.amazon.com/what-is/edge-computing/

[4] https://www.ibm.com/topics/edge-computing

[5] https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-edge-computing

[6] https://en.wikipedia.org/wiki/Edge_computing

[7] https://www.cloudflare.com/learning/serverless/glossary/what-is-edge-computing/

[8] https://www.spiceworks.com/tech/edge-computing/articles/what-is-edge-computing/amp/

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