An Implementation of the DCF-MAC protocol based VANET

Sahayaraj, J. Martin; Ramesh, T. K.; Suresh, J.; Pratha, S. Jaya

doi:10.1109/icssit53264.2022.9716500

Cited by 19 publications

(1 citation statement)

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“…Road Side Units (RSU) 5 can either be manually positioned or positioned using GPS signals. RSUs have the same transmitter, receiver, antenna, sensors, and processor as On‐Board Units 6,7 . RSUs greatly increase the accessibility of communication due to VANET's totally mobile topology and adjustable latency.…”

Section: Introductionmentioning

confidence: 99%

NLOS node localization for improving warning message distribution in VANETS

Arunkumar,

Chinnasamy

2023

Concurrency and Computation

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SummaryVehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques .

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Section: Introductionmentioning

confidence: 99%