An Effective Congestion Control System for Vehicular Adhoc Networks Using Multidimensional Data Clustering

  • Mohammad PashaEmail author
  • Huda Banu Baig
  • Mohd Umarfarooq
  • Khaleel Ur Rahman Khan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


VANETs are proved to be the adequate solution to High traffic problems. Controlling congestion will effectively reduce the delay, data loss and enhance the stability of ITS. This proposal introduces an intelligent congestion control strategy which deals with the three phases to detects and control congestion. First phase detects congestion by measuring the Channel Busy Time. In the second phase the information is collected, filtered and clustered using K-means machine learning algorithm which classifies information based on type and deadline. The third phase is to set right priorities for each cluster and send notifications to the High speed vehicle group with greater priority. The Strategy focuses on controlling congestion by sending right cluster of messages to appropriate vehicles. Results from runtime highlights that the projected strategy master existing in terms of delay, throughput and also improves the efficiency of the system.


VANETS Intelligent transportation system Quality of service Traffic congestion Machine learning Multidimensional K means algorithm 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mohammad Pasha
    • 1
    Email author
  • Huda Banu Baig
    • 1
  • Mohd Umarfarooq
    • 1
  • Khaleel Ur Rahman Khan
    • 2
  1. 1.Muffakham Jah College of Engineering and TechnologyHyderabadIndia
  2. 2.ACE Engineering CollegeHyderabadIndia

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