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一种基于物联网的雾计算模型。

An IoT-Based Fog Computing Model.

作者信息

Ma Kun, Bagula Antoine, Nyirenda Clement, Ajayi Olasupo

机构信息

ISAT Laboratory, Department of Computer Science, University of the Western Cape, Bellville 7535, South Africa.

出版信息

Sensors (Basel). 2019 Jun 21;19(12):2783. doi: 10.3390/s19122783.

DOI:10.3390/s19122783
PMID:31234280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6630307/
Abstract

The internet of things (IoT) and cloud computing are two technologies which have recently changed both the academia and industry and impacted our daily lives in different ways. However, despite their impact, both technologies have their shortcomings. Though being cheap and convenient, cloud services consume a huge amount of network bandwidth. Furthermore, the physical distance between data source(s) and the data centre makes delays a frequent problem in cloud computing infrastructures. Fog computing has been proposed as a distributed service computing model that provides a solution to these limitations. It is based on a para-virtualized architecture that fully utilizes the computing functions of terminal devices and the advantages of local proximity processing. This paper proposes a multi-layer IoT-based fog computing model called IoT-FCM, which uses a genetic algorithm for resource allocation between the terminal layer and fog layer and a multi-sink version of the least interference beaconing protocol (LIBP) called least interference multi-sink protocol (LIMP) to enhance the fault-tolerance/robustness and reduce energy consumption of a terminal layer. Simulation results show that compared to the popular max-min and fog-oriented max-min, IoT-FCM performs better by reducing the distance between terminals and fog nodes by at least 38% and reducing energy consumed by an average of 150 KWh while being at par with the other algorithms in terms of delay for high number of tasks.

摘要

物联网（IoT）和云计算是最近改变了学术界和工业界并以不同方式影响我们日常生活的两项技术。然而，尽管它们有影响，但这两项技术都有其缺点。云服务虽然便宜且方便，但会消耗大量网络带宽。此外，数据源与数据中心之间的物理距离使得延迟成为云计算基础设施中经常出现的问题。雾计算已被提出作为一种分布式服务计算模型，为这些限制提供了解决方案。它基于一种准虚拟化架构，充分利用终端设备的计算功能和本地近端处理的优势。本文提出了一种基于物联网的多层雾计算模型，称为物联网 - FCM，它使用遗传算法在终端层和雾层之间进行资源分配，并使用一种称为最小干扰多信标协议（LIMP）的最小干扰信标协议（LIBP）的多汇聚节点版本来增强终端层的容错性/鲁棒性并降低能耗。仿真结果表明，与流行的最大最小算法和面向雾的最大最小算法相比，物联网 - FCM通过将终端与雾节点之间的距离至少减少38％，平均能耗降低150千瓦时，同时在大量任务的延迟方面与其他算法相当，表现更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/ea6f11aa242a/sensors-19-02783-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/4ddc0375df52/sensors-19-02783-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/03cdd1321ed5/sensors-19-02783-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/4c31b4c581c9/sensors-19-02783-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/c84b8d227139/sensors-19-02783-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/22b8dfe6db87/sensors-19-02783-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/9914c2254f4b/sensors-19-02783-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/a62d94fa969d/sensors-19-02783-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/d6958372d4f8/sensors-19-02783-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/d73c306181d5/sensors-19-02783-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/ea6f11aa242a/sensors-19-02783-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/4ddc0375df52/sensors-19-02783-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/03cdd1321ed5/sensors-19-02783-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/4c31b4c581c9/sensors-19-02783-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/c84b8d227139/sensors-19-02783-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/22b8dfe6db87/sensors-19-02783-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/9914c2254f4b/sensors-19-02783-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/a62d94fa969d/sensors-19-02783-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/d6958372d4f8/sensors-19-02783-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/d73c306181d5/sensors-19-02783-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f77f/6630307/ea6f11aa242a/sensors-19-02783-g010.jpg

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