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

Jianhua He, Jian Wei, Kai Chen, Zuoyin Tang, Yi Zhou, Yan Zhang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)677 - 686
JournalIEEE Internet of Things Journal
Issue number2
Early online date11 Jul 2017
Publication statusPublished - 1 Apr 2018

Bibliographical note

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  • data Analytics
  • fog computing
  • internet of things
  • quality of services
  • raspberry Pi
  • smart Cities
  • spark


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