Multi-tier fog computing with large-scale IoT data analytics for smart cities


Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multi-tier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multi-tier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. QoS aware admission control, offloading and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multi-tier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > Adaptive communications networks research group
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: data Analytics,fog computing,internet of things,quality of services,raspberry Pi,smart Cities,spark,Signal Processing,Information Systems,Hardware and Architecture,Computer Science Applications,Computer Networks and Communications,Information Systems and Management
Publication ISSN: 2327-4662
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2018-04-01
Published Online Date: 2017-07-11
Accepted Date: 2017-07-08
Authors: He, Jianhua (ORCID Profile 0000-0002-5738-8507)
Wei, Jian
Chen, Kai
Tang, Zuoyin (ORCID Profile 0000-0001-7094-999X)
Zhou, Yi
Zhang, Yan



Version: Accepted Version

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