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Wireless Personal Communications

Erschienen in:

04.05.2022

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes

verfasst von: Lokesh M. Giripunje, Abhay Vidyarthi, Shishir Kumar Shandilya

Erschienen in: Wireless Personal Communications | Ausgabe 4/2022

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Abstract

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly.

Vorheriger Artikel Routing Algorithms for MANET-IoT Networks: A Comprehensive Survey

Nächster Artikel An Energy-Efficient Load Balancing Approach for Scientific Workflows in Fog Computing

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Titel: Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes
verfasst von: Lokesh M. Giripunje
Abhay Vidyarthi
Shishir Kumar Shandilya
Publikationsdatum: 04.05.2022
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 4/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09723-w

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