Research Repository

Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

He, Jianhua and Wei, Jian and Chen, Kai and Tang, Zuoyin and Zhou, Yi and Zhang, Yan (2018) 'Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities.' IEEE Internet of Things Journal, 5 (2). pp. 677-686. ISSN 2327-4662

Multi_tier_fog_computing_with_large_scale_IoT_data_analytics_for_smart_cities.pdf - Accepted Version

Download (752kB) | Preview


Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier 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. Quality of services (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 multitier 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.

Item Type: Article
Uncontrolled Keywords: Data analytics; fog computing; Internet of Things (IoT); quality of services (QoS); Raspberry Pi; smart cities; Spark
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 06 Oct 2020 11:56
Last Modified: 15 Jan 2022 01:31

Actions (login required)

View Item View Item