Drone-Assisted Vehicular Network Architecture Exploiting Road Weather Information
Tahir, Muhammad Naeem; Katz, Marcos; Pouttu, Ari (2024-02-26)
IEEE
26.02.2024
M. N. Tahir, M. Katz and A. Pouttu, "Drone-Assisted Vehicular Network Architecture Exploiting Road Weather Information," GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 2997-3002, doi: 10.1109/GLOBECOM54140.2023.10437616
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406114372
https://urn.fi/URN:NBN:fi:oulu-202406114372
Tiivistelmä
Abstract
Since the last decade, researchers have been continuously trying to implement and evaluate the performance of Drone Assisted Vehicular Network (DAVN). A DAVN efficiently integrates the networking and communication technologies of drones with connected vehicles. DAVNs have a huge potential to offer a wide range of features for Intelligent Transport Systems (ITS) applications to improve traffic safety on roads. In this paper, we first discuss the architecture of a DAVN and outline its potential services for vehicular networks. Drones cooperate with infrastructure and vehicles to improve Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) network coverage, data collection capability, and efficiency of communication interworking. In this paper, we demonstrate the DAVN concept with regard to Drone-to-Vehicle (D2V) and Drone-to-Infrastructure (D2I) communications utilizing road meteorological data exchange. To perform these pilot scenarios, we use real-time weather and traffic data collected during our pilot measurements in Northern Finland. The executed and generated test scenarios are added to Wireshark and NS-2 (Network Simulators) to evaluate the performance of ITS-G5 and 5G Test Network (5GTN). The performance evaluation for DAVN is carried out by considering the following parameters: end-to-end delay, packet delivery ratio (PDR), packet loss and average throughput. Our results revealed that ITS-G5 performs better and more efficiently than 5G in D2V PDR scenarios, and 5G performs well in D2I PDR scenarios. Moreover, the 5G network presents better performance in the average throughput and D2I (in our case drone-to-RWS (Road-Weather-Station)) delay scenario in contrast to ITS-G5, and this is due to vehicles, haphazard nature of test track and the distance between the cars.
Since the last decade, researchers have been continuously trying to implement and evaluate the performance of Drone Assisted Vehicular Network (DAVN). A DAVN efficiently integrates the networking and communication technologies of drones with connected vehicles. DAVNs have a huge potential to offer a wide range of features for Intelligent Transport Systems (ITS) applications to improve traffic safety on roads. In this paper, we first discuss the architecture of a DAVN and outline its potential services for vehicular networks. Drones cooperate with infrastructure and vehicles to improve Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) network coverage, data collection capability, and efficiency of communication interworking. In this paper, we demonstrate the DAVN concept with regard to Drone-to-Vehicle (D2V) and Drone-to-Infrastructure (D2I) communications utilizing road meteorological data exchange. To perform these pilot scenarios, we use real-time weather and traffic data collected during our pilot measurements in Northern Finland. The executed and generated test scenarios are added to Wireshark and NS-2 (Network Simulators) to evaluate the performance of ITS-G5 and 5G Test Network (5GTN). The performance evaluation for DAVN is carried out by considering the following parameters: end-to-end delay, packet delivery ratio (PDR), packet loss and average throughput. Our results revealed that ITS-G5 performs better and more efficiently than 5G in D2V PDR scenarios, and 5G performs well in D2I PDR scenarios. Moreover, the 5G network presents better performance in the average throughput and D2I (in our case drone-to-RWS (Road-Weather-Station)) delay scenario in contrast to ITS-G5, and this is due to vehicles, haphazard nature of test track and the distance between the cars.
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