Detecting Vehicular Congestion Using Fog Computing

Authors

  • Geeta Amol Patil Associate Professor, Department of Information Science & Engineering, Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management, Bengaluru, Karnataka, India
  • Surekha K.B. Associate Professor, Department of Information Science & Engineering, Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management, Bengaluru, Karnataka, India
  • Mohan B.A. Assistant Professor, Department of Information Science & Engineering, Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management, Bengaluru, Karnataka, India
  • Anil Kumar Student, Department of Information Science & Engineering, Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management, Bengaluru, Karnataka, India
  • Niharika D. Student, Department of Information Science & Engineering, Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management, Bengaluru, Karnataka, India

Keywords:

Fog Computing, Internet of Vehicles (IoV), Traffic, Congestion

Abstract

Traffic congestion has become a major challenge for modern transportation systems, leading to increased travel time, fuel consumption and air pollution. To address this issue, Internet of Vehicles (IoV) has emerged as a promising technology for detecting traffic congestion in real-time. This study discusses a research investigation that explores the utilization of Fog Computing principles to detect traffic congestion. The proposed system utilizes sensors to capture traffic data and send it to the Fog nodes for processing. The data is analysed using machine learning algorithms to identify congestion patterns and provide real-time alerts to drivers and traffic management centres.

References

Janahan SK, Veeramanickam MR, Arun S, Narayanan K, Anandan R, Parvez SJ. IoT based smart traffic signal monitoring system using vehicles counts. Int J Eng Technol. 2018; 7(2.21): 309–12.

PV A, Mani V, Sankaranarayanan S. IoT based traffic signalling system. Int J Appl Eng Res. 2017; 12(19): 8264–9.

Frank A, Al Aamri YS, Zayegh A. IoT based smart traffic density control using image processing. In 2019 IEEE 4th MEC International Conference on Big Data and Smart City (ICBDSC). 2019 Jan 15; 1–4.

Sánchez-Corcuera R, Nuñez-Marcos A, Sesma-Solance J, Bilbao-Jayo A, Mulero R, Zulaika U, Azkune G, Almeida A. Smart cities survey: Technologies, application domains and challenges for the cities of the future. Int J Distrib Sens Netw. 2019 Jun; 15(6): 1550147719853984.

Bauza R, Gozalvez J, Sanchez-Soriano J. Road traffic congestion detection through cooperative vehicle-to-vehicle communications. In IEEE Local Computer Network Conference. 2010 Oct 10; 606–612.

Bauza R, Gozálvez J. Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. J Netw Comput Appl. 2013 Sep 1; 36(5): 1295–307.

He Z, Zhang D. Cost-efficient traffic-aware data collection protocol in VANET. Ad Hoc Netw. 2017 Feb 1; 55: 28–39.

Hameed AR, ul Islam S, Ahmad I, Munir K. Energy-and performance-aware load-balancing in vehicular fog computing. Sust Comput: Inform Systems (SUSCOM). 2021 Jun 1; 30: 100454.

Alamer A, Deng Y, Wei G, Lin X. Collaborative security in vehicular cloud computing: A game theoretic view. IEEE Netw. 2018 Jun 4; 32(3): 72–7.

Song J, Lee S, Kim J. Inference attack on browsing history of twitter users using public click analytics and twitter metadata. IEEE Trans Dependable Secure Comput. 2014 Dec 18; 13(3): 340–54.

Bousselham M, Benamar N, Addaim A. A new security mechanism for vehicular cloud computing using fog computing system. In 2019 IEEE International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). 2019 Apr 3; 1–4.

Thakur A, Malekian R. Fog computing for detecting vehicular congestion, an internet of vehicles based approach: A review. IEEE Intell Transp Syst Mag. 2019 Mar 17; 11(2): 8–16.

Arroub A, Zahi B, Sabir E, Sadik M. A literature review on Smart Cities: Paradigms, opportunities and open problems. In 2016 IEEE International conference on wireless networks and mobile communications (WINCOM). 2016 Oct 26; 180–186.

Knud Lasse Lueth. (2018 Aug 8). State of the IoT 2018: Number of IoT devices now at 7B – Market accelerating. [Online]. IoT Analytics. Available from: https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/

Hollands RG. Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? In The Routledge companion to smart cities; Routledge: 2020 Mar 27; 179–199.

Farooqi AM, Alam MA, Hassan SI, Idrees SM. A fog computing model for VANET to reduce latency and delay using 5G network in smart city transportation. Appl Sci. 2022 Feb 17; 12(4): 2083.

Raza S, Wang S, Ahmed M, Anwar MR. A survey on vehicular edge computing: architecture, applications, technical issues, and future directions. Wirel Commun Mob Comput. 2019 Feb 24; 2019: 3159762.

Farooqi AM, Hassan SI, Alam MA. Sustainability and fog computing: applications, advantages and challenges. In 2019 IEEE 3rd International Conference on Computing and Communications Technologies (ICCCT). 2019 Feb 21; 18–23.

Pokhrel SR. Software defined internet of vehicles for automation and orchestration. IEEE Trans Intell Transp Syst. 2021 May 25; 22(6): 3890–9.

Sharma S, Kaushik B. A survey on internet of vehicles: Applications, security issues & solutions. Veh Commun. 2019 Dec 1; 20: 100182.

Downloads

Published

2023-09-05

Issue

Vol. 10 No. 2 (2023): Journal of Open Source Developments

Section

Review Article