The increasing of complexity of urban traffic call for innovative approaches in monitoring and management. While legacy, ground-based methods struggle with problems like a lack of complete sensing as well as latency, preventing real-time insights and response ability.This article investigate how cooperative Device-to-Device (D2D) communications with aid of Unmanned Aerial Vehicle (UAV) could potentially disrupt the conventional drone-assisted traffic monitoring in 5G networks. In this work, we introduce a new system to utilize the data collection possibility of drones in a distributed way, which can facilitate more effective urban data acquisition and assimilation.The article conducted extensive simulations in various traffic conditions to demonstrate the effectiveness of our system. The results were compared with traditional ground-based monitoring metrics, while against a drone-based system that without cooperative D2D communications.The proposed approach was evaluated through a number of wide-range simulations in diverse traffic scenarios. Simulation results indicate significant improvements in data collection and distribution over conventional strategies. Our proposed system on average improved the throughput by 35%, and latency by 45% over traditional ground-based monitoring methods. The energy efficiency of the system was also investigated, considering the UAVs' short flight durations. Results also showed the D2D communication technique, which increased through boosting by using drones, helps reduce energy consumption by 25% relative to traditional methods, maximizing drone uptime.This study indicates that cooperative D2D communications of drones can be useful for traffic monitoring. This technique helps to reduce the latency and atrocity with which data are collected for intelligent transport systems, thereby enhances the traffic management and road safety.


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    Title :

    Efficient Drone-Assisted Cooperative D2D Communications in 5G Networks for Traffic Monitoring




    Publication date :

    2024-10-30


    Size :

    1813059 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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