Vehicular networks are used in maximum of the intelligent communication oriented applications. Vehicles which are present in the network are high speed and its mobile are highly unpredictable and that leads to reducing the communication quality during the peak traffic hours. In order to overcome these drawbacks, and Unmanned Aerial Vehicles (UAV's) which is otherwise called as flying base station is incorporated with the vehicular network. The UAV assistant vehicles are able to communicate in the efficient manner that increases inter and intra vehicular communication performance. Due to the growing number of vehicles, the network still requires improvement to standardize vehicle monitoring. In this article, optimization-based UAV placement (EOUAV) is proposed in the vehicular network to combine successful UAV deployment with better ant colony optimization. This procedure deploys UAVs appropriately, increasing network flexibility and communication quality. Energy consumption, data success rate, data loss rate, and throughput are used to analyze this suggested EOUAV's experimental results in ns2 software. The comparison study shows that the proposed EOUAV outperformed baseline approaches in data success rate and throughput.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Experimental Demonstration of Optimization based UAV placement in Vehicular Networks




    Publication date :

    2025-06-05


    Size :

    469861 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    VEHICULAR BATTERY PLACEMENT STRUCTURE

    KAWASUMI SHINJI / YOSHIDA HIROAKI / KUBOZONO MUNEHIRO | European Patent Office | 2018

    Free access

    Learning Sensor Placement from Demonstration for UAV networks

    Benbihi, Assia / Geist, Matthieu / Pradalier, Cédric | ArXiv | 2019

    Free access

    Context-Aware Service Placement at the Edge in Vehicular Networks

    Zhang, Wanlu / Tao, Chenhui / Luo, Jingjing et al. | IEEE | 2023