VANET (ITS-G5 & 5G Test Network) with Drone-assisted Communication Using Road Weather Information

Abstract

In recent years, the research community has shown increasingly interest in the study, design and implementation of DAVNs (Drone-assisted Vehicular-Network). DAVN can play a crucial role to provide a broad range of vital characteristics for Intelligent Transport Systems (ITS) applications to enhance the road traffic safety. A DAVN provides a dynamic platform by effectively combining the ground networks, namely Connected Vehicles (CV) and aerial communication technologies of drones. In DAVN, due to the small sizes of drones, payload constraints as well as capabilities and flight time limitations, cooperating with traffic infrastructure and vehicles is very important. DAVNs help to improve the Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication range, the information gathering capability, and the efficiency of vehicular networking. In this paper, we develop a novel cooperative DAVN architecture consisting of drones, vehicles, Road Weather Stations (RWS), on-board embedded sensors, processing entities, Global-Positioning-System (GPS), and communication capabilities. We use real-time field measurements of V2V and V2I communications considering weather and vehicular traffic information for drone assisted vehicular communication simulations in Network Simulator-2 (NS-2). DAVN simulations are carried out based on measurements on a real environment (1.7km long test track) at the Finnish Meteorological Institute (FMI) in Sodankylä, Finland. The performance of DAVN was evaluated by considering three parameters: packet loss, network latency and average throughput. Simulation results revealed that ITS-G5 performance is stable in short range, but in long range DAVN, the 5G Test Network (5GTN) and its 4G feature outperformed ITS-G5. Moreover, we identified key challenges in the design and implementation of DAVNs in real environments.