| Autor: |
DHAKAD, BHUPENDRA, AKASHE, SHYAM, OJHA, SHAILENDRA SINGH, MISHRA, SADHANA |
| Předmět: |
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| Zdroj: |
Journal of Active & Passive Electronic Devices; 2024, Vol. 18 Issue 1, p65-89, 25p |
| Abstrakt: |
Vehicular Communication becomes growing research field due to continuously increasing vehicles density and that causes traffic jams, road accidents etc. These problems can be overcome up to some limit by proper shearing of information about their location, speed and acceleration through the periodic messages transmission among vehicles but as the vehicle density increases number of transmitted messages through channel increase and that cause's channel congestion. To improve the channel performance a risk aware data congestion limiting technique (RA-DCLT) is implemented. This technique uses the concept of machine learning with data transmission controlling mechanism and provides the efficient result from the existing congestion techniques by evaluating packet delivery ratio, information loss and throughput. RA-DCLT technique works on the principle of K Mean algorithm with some modification to grouping vehicles and concept of channel busy ratio (CBR). R-DCLT works in three sections, first it groups the nearest vehicles on road by K Mean algorithm with some modifications then in second section it identified the congestion on channel by correlating the instant data load on channel with bandwidth then in last section all the vehicles select data rate by keeping very less risk possibility to minimised congestion on channel. For the evaluation of RA-DCLT, road network is designed by the SUMO software, the proposed idea for congestion minimization is implemented through MATLAB and performance matrix of RA-DCLT is calculated through NS2. The results are the evidence that RA-DCLT is risk aware efficient congestion limiting technique then existing congestion control technique. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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