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Taxonomy of Edge Computing: Challenges, Opportunities, and Data Reduction Methods

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Edge Computing

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Maharishi Arvind Institute of Engineering & Technology, Jaipur, Rajasthan, India

    Kusumlata Jain & Smaranika Mohapatra

  2. Rajasthan Technical University, Kota, Rajasthan, India

    Kusumlata Jain & Smaranika Mohapatra

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  1. Kusumlata Jain
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  2. Smaranika Mohapatra
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  1. Computer Engineering Department, Antalya Bilim University, Antalya, Turkey

    Fadi Al-Turjman

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Jain, K., Mohapatra, S. (2019). Taxonomy of Edge Computing: Challenges, Opportunities, and Data Reduction Methods. In: Al-Turjman, F. (eds) Edge Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99061-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-99061-3_4

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