Mobility Prediction issues and Challenges in Vehicular Ad-Hoc Networks

Authors

  • Kishor N Tayade  Assistant Professor, Department of IT, Govt. College of Engineering Amravati, Maharashtra, India
  • Dr. M U Kharat  Professor, CSE Department, MET IoE Bhujbal Knowledge City Nashik, Maharashtra, India
  • Dr. S V Gumaste  Professor, Department of IT, MET IoE Bhujbal Knowledge City Nashik, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2063216

Keywords:

Mobility, Mobility Prediction, VANET, Topology, OBU, RSU, Intelligent Transportation System, Microscopic, Macroscopic.

Abstract

Vehicular Ad Hoc Networks (VANETs) are gaining much attention from both industry and academia communities as an emerging technology. VANET is composed of self-regulating nodes where the vibrant node mobility changes the network topology frequently. The important aim of VANET is to upgrade road safety conditions to minimize extent of road mishaps. The nodes are very mobile in vehicular ad hoc network so routing becomes complex job. As the VANET nodes move very fast the topology changes accordingly. So node mobility prediction in VANET is a very important issue. Further, prediction of mobility helps to estimate the stable path between the nodes which leads to better routing. Estimating secure paths among the routing of nodes perform in a better way, thereby reducing the overhead and minimizing interruptions in connections. This paper explores VANET's basic architecture and discusses a number of current mobility prediction techniques, and concludes with performance analysis of existing routing protocols and proposed mobility prediction methods.

References

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Published

2020-06-30

Issue

Section

Research Articles

How to Cite

[1]
Kishor N Tayade, Dr. M U Kharat, Dr. S V Gumaste, " Mobility Prediction issues and Challenges in Vehicular Ad-Hoc Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.992-999, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2063216