IoT 19(17): e3

Research Article

IoT enabled Smart Fog Computing for Vehicular Traffic Control

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  • @ARTICLE{10.4108/eai.31-10-2018.162221,
        author={Akashdeep Bhardwaj and Sam Goundar},
        title={IoT enabled Smart Fog Computing for Vehicular Traffic Control},
        journal={EAI Endorsed Transactions on Internet of Things},
        keywords={Fog Computing, Edge Computing, Internet of Things, Fog Security, Cloudlets, IoT},
  • Akashdeep Bhardwaj
    Sam Goundar
    Year: 2019
    IoT enabled Smart Fog Computing for Vehicular Traffic Control
    DOI: 10.4108/eai.31-10-2018.162221
Akashdeep Bhardwaj1,*, Sam Goundar2
  • 1: University of Petroleum and Energy Studies, Dehradun, India
  • 2: The University of South Pacific, Suva, Fiji
*Contact email:


INTRODUCTION: Internet was initially designed to connect web sites and portals with data packets flowing over the networks for communications at corporate levels. Over time, live video streaming, real-time data and voice is being offered over hosted Clouds for business entertainment. Enterprise applications like Office 365, banking and e-commerce are available over smartphones. With the advent of Fog Computing and Internet of Things, corporate enterprises and non-IT industries see potential in this technology. Billions of Internet-enabled devices, globally distributed nodes, embedded sensor gateways transmit real-time generated over the internet to the cloud data centres. Cloud environments are not designed to handle this level of data that is being generated and Computing limits are being severely tested. Fog Computing has the potential to be the go-to option for Cloud service delivery.

OBJECTIVES: This paper reviewed existing research works and presents unique Smart Fog Computing based taxonomy. The authors also implemented experimental setup for Smart Cities using Smart Fog Computing for controlling Vehicular traffic.

METHODS: Smart Vehicular Management is viable use case for Fog and IoT technology. The authors designed and implemented two experimental setups. The first setup involvesstandard Cloud implementation and the second setup employs Fog Computing implemented using IoT Sensor nodes to compare the performance of the Vehicle Management Fog application regarding the Response time and Bandwidth Consumed. The architecture and implementation involved deploying 50 IoT sensors nodes across the university areas and routes.

RESULTS: The main results obtained in this paper are the following. As compared to Cloud computing, on deploying Fog Computing and IoT devices:
  • End-to-End Processing time dropped from 29.44 to 6.7 seconds → almost 77% less
  • Number of hops traversed reduced from 56 to 4 hops → almost 92% less
  • Bandwidth usage dropped from 247 to 8 kbps → almost 96.7% less

CONCLUSION: From the experimental setups as compared to Cloud computing, the Fog and IoT processes the traffic data locally on the edge devices, which reduces the end-to-end time.