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SWIM Blog

    4 Real-world Use Cases to Differentiate Edge Computing & Fog Computing (Part 2)

    Posted on Dec 5, 2017 11:34:03 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Edge Computing, in Fog Computing

    This is part 2 of a two part series. Read more about Fog & Edge Computing in Part 1.

    Scores of articles discuss the difference between Edge and Fog computing, but their close proximity to each other in application architectures has led to significant confusion, often with the two terms being used interchangeably. Generally speaking, the difference between Fog and Edge computing concerns where data processing capabilities are located in the physical network architecture of an application. According to Automation Weekly:

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    4 Real-world Use Cases to Differentiate Edge Computing & Fog Computing (Part 1)

    Posted on Dec 1, 2017 10:29:35 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Edge Computing, in Fog Computing

    This is part 1 of a two part series. Read more about Fog & Edge Computing in Part 2.

    Over the past few years, there have been scores of articles discussing the difference between Edge and Fog computing. Though Fog and Edge are distinct concepts, their close proximity to each other in real-world deployments has led to significant confusion, often with the two terms being used interchangeably. Generally speaking, the difference between Fog and Edge computing concerns where data processing capabilities are located in the physical network architecture of an application. The most concise definitions I’ve come across are from an article by Automation Weekly in 2016, which contained interviews with industrial software executives and marketers. According to the article:

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    Key Takeaways From SDX Central's 2017 “Innovations in Edge Computing and MEC” Report (Part 3)

    Posted on Nov 14, 2017 10:45:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Edge Computing

    This is Part 3 of a three part series. Read more about innovations in Edge Computing in Part 1 and Part 2.

    IOT technologies are helping decision-makers to quantify new insights from industrial equipment, workers, buildings, and other sources. These real-time edge data insights have the potential to reduce operating costs, improve efficiency, or deliver new capabilities. As businesses increasingly turn to IOT and analytics technologies in order to maximize the efficiency industrial environments, it’s no surprise that Edge Computing has become one of the biggest technology buzzwords of 2017. With massive volumes of data being generated by connected sensors, the Edge Computing model has proven to be an important building block for the industrial analytics applications of the future.

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    Key Takeaways From SDX Central's 2017 “Innovations in Edge Computing and MEC” Report (Part 2)

    Posted on Nov 7, 2017 10:53:23 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Edge Computing

    This is Part 2 of a three part series. Read more about innovations in Edge Computing in Part 1 and Part 3 (coming soon!).

    IOT technologies are helping decision-makers to quantify new insights from industrial equipment, workers, buildings, and other sources. These real-time edge data insights have the potential to reduce operating costs, improve efficiency, or deliver new capabilities. As businesses increasingly turn to IOT and analytics technologies in order to maximize the efficiency industrial environments, it’s no surprise that Edge Computing has become one of the more hyped buzzwords of 2017. With massive volumes of data being generated by connected sensors, the Edge Computing model has proven to be an important buildingblock for the industrial analytics applications of the future.

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    Key Takeaways From SDX Central's 2017 “Innovations in Edge Computing and MEC” Report (Part 1)

    Posted on Nov 2, 2017 11:58:12 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Edge Computing

    This is part 1 of a three part series. Read more about innovations in Edge Computing in parts 2 and 3 (coming soon!).

    Call it the Great Quantification. IOT technologies have made it possible to gather valuable insights from industrial equipment, workers, buildings, and other sources. Decision-makers are now armed with vast amounts of information about how their businesses operate. These real-time edge data insights have the potential to reduce operating costs, improve efficiency, or deliver new capabilities. As businesses increasingly turn to IOT and analytics technologies in order to maximize the efficiency industrial environments, it’s no surprise that Edge Computing has become one of the more hyped buzzwords of 2017.

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    Three Ways Edge Computing Will Affect the Future of Smart Cities

    Posted on Oct 20, 2017 11:24:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

    Discussions are occurring in major cities throughout the world between city planners, technologists, and citizens about how to make their city a Smart City. With ambitions for creating a better quality of life for citizens, Smart City planners must tackle a complex landscape of sensors and data processing technologies. Due to the rapid pace of innovation with technologies such as Edge Computing, Machine Learning, and Digital Twins, it’s often hard to keep track of how recent developments will affect the cities of the future.

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    Key Takeaways from “The Forrester Wave: Streaming Analytics” (Part II)

    Posted on Oct 20, 2017 11:24:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Swim AI

    This is a part 2 of a two part series. Read more about the future of streaming analytics in part 1.

    While the data lakes landscape has matured over the last few years, the streaming analytics market is continuing to develop as more enterprises embrace the use of streaming analytics to provide real-time insights for their business systems. Early returns suggest that embracing streaming analytics in industry can lead to significant efficiency gains and decreased operational costs. For example, a study by McKinsey & Co. found that “using real-time data to predict and prevent breakdowns can reduce [manufacturing] downtime by 50 percent.” With real-world cost savings at stake, the momentum toward real-time has never been stronger.

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    Key Takeaways from “The Forrester Wave: Streaming Analytics” (Part I)

    Posted on Oct 18, 2017 11:15:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics

    This is a part 1 of a two part series. 

    While the data lakes landscape has matured over the last few years, the streaming analytics market is continuing to develop as more enterprises embrace the use of streaming analytics to provide real-time insights for their business systems. Early returns suggest that embracing streaming analytics in industry can lead to significant efficiency gains and decreased operational costs. For example, a study by McKinsey & Co. found that “using real-time data to predict and prevent breakdowns can reduce [manufacturing] downtime by 50 percent.” With real-world cost savings at stake, the momentum toward real-time has never been stronger.

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    Future of Smart Cities and Real-time Traffic Predictions (Part II)

    Posted on Oct 13, 2017 11:05:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

    This is a part 2 of a two part series. Read more about the future of Smart Cities Part 1.

    New innovations in Machine Learning, Edge Computing, and real-time data analytics are shaping the development of Smart Cities projects around the world. One benchmark use case for Smart Cities efforts is that of Intelligent Transportation Systems (ITS), which include the use of connected vehicles and transportation infrastructure to automate or optimize transportation systems. With 58% of in-development Smart Cities projects relating to either Smart Buildings or Smart Transportation efforts, and 250 million connected vehicles expected to be on the road by the year 2020, the adoption of Smart Cities technologies will have a transformative effect on the transportation systems of the future.

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    Future of Smart Cities and Real-time Traffic Predictions (Part I)

    Posted on Oct 11, 2017 11:00:00 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Smart Cities, in Edge Analytics, in Swim AI

    This is a part 1 of a two part series. 

    New innovations in Machine Learning, Edge Computing, and real-time data analytics are shaping the development of Smart Cities projects around the world. One benchmark use case for Smart Cities efforts is that of Intelligent Transportation Systems (ITS), which include the use of connected vehicles and transportation infrastructure to automate or optimize transportation systems. With 58% of in-development Smart Cities projects relating to either Smart Buildings or Smart Transportation efforts, and 250 million connected vehicles expected to be on the road by the year 2020, the adoption of Smart Cities technologies will have a transformative effect on the transportation systems of the future.

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