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Adaptive scheduled partitioning technique for reliable emergency message broadcasting in VANET for intelligent transportation systems

Authors :
M. Ramya Devi
I. Jasmine Selvakumari Jeya
S. Lokesh
Source :
Automatika, Vol 64, Iss 2, Pp 341-354 (2023)
Publication Year :
2023
Publisher :
Taylor & Francis Group, 2023.

Abstract

This paper aims to enable accurate and reliable emergency message broadcast in Vehicular Ad hoc Network (VANET). The VANET is the most common topology used in Intelligent Transportation Systems (ITS), where changes in standard topology due to the mobility of nodes create challenges in broadcasting the emergency message and efficient data delivery in both highway and urban scenarios. The main problems in urban scenarios are channel contention, message redundancy and road structure. To obtain information, broadcast protocols for VANET typically use beacon messages, which are distributed among the vehicles. When multiple vehicles transmit messages at the same time, a broadcast storm occurs and vehicles experience message delivery failure. To address this problem, Adaptive Scheduled Partitioning and Broadcasting Technique (ASPBT) for emergency message broadcast and beacon retransmissions for message reliability were proposed. This protocol dynamically modifies several partitions and beacon periodicity to reduce the number of retransmissions. Later, the partition size is determined by estimating the network transmission density of each partition schedule via the Black Widow Optimization (BWO) algorithm is proposed. The simulation is carried out with different network densities at the vehicle speed of 110 km/h, a direct path length of 12 km under a four-way direction path and performance analysis was performed.

Details

Language :
English
ISSN :
00051144 and 18483380
Volume :
64
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Automatika
Publication Type :
Academic Journal
Accession number :
edsdoj.1f5ed8c507e142438d864a055c31c760
Document Type :
article
Full Text :
https://doi.org/10.1080/00051144.2022.2140392