Publication | Closed Access
An RSU Deployment Strategy Based on Traffic Demand in Vehicular <i>Ad Hoc</i> Networks (VANETs)
75
Citations
40
References
2021
Year
Rsu Deployment StrategyVehicle CommunicationEngineeringInternet Of VehicleTraffic DemandConnected CarAutomatic VehicleVehicle-to-everything CommunicationVirtual Road NetworkSystems EngineeringVehicle NetworkVehicular NetworksComputer ScienceConnected VehiclesMobile Communication VehicleTransportation EngineeringTransportation Systems
The rapid rise of connected autonomous vehicles has made vehicular ad hoc networks (VANETs) a critical research area, where roadside units (RSUs) support vehicle‑to‑infrastructure communication but suboptimal deployment can degrade network efficiency and coverage. This study proposes an RSU deployment strategy that simultaneously optimizes network efficiency and coverage by aligning RSU placement with traffic demand. The strategy models the trade‑off between average data‑delivery delay and vehicle coverage, and its effectiveness is validated through simulations on a 4 km × 4 km virtual road network. Simulations show that covering 25 % of road segments serves most vehicles and reduces delay, that higher traffic demand requires more RSUs to achieve the same benefit, and that early RSU investment is more cost‑effective, providing guidance on where and how to prioritize RSU deployment.
The rapid development of connected automatic vehicle (CAV) technology makes vehicular <i>ad hoc</i> networks (VANETs) an urgently needed research field. It includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) message flows. A roadside unit (RSU) is an important infrastructure for V2I communication and provides roadside information services for CAVs. However, an unoptimal RSU deployment may result in RSUs failing to improve the efficiency of VANETs and compromising the capability of service to most vehicles. Motivated by this observation, this study focuses on balancing the two objectives of efficiency and coverage and establishing an RSU deployment strategy based on traffic demand. In detail, this model optimizes both the average data delivery delay in VANETs and the number of vehicles covered by RSUs. The effectiveness of the method is verified by simulation in a 4 km <inline-formula> <tex-math notation="LaTeX">${\times }4$ </tex-math></inline-formula> km virtual road network. We also found that: 1) if 25% of the road segments in the road network are covered by RSUs, most vehicles can be served, and the delay of VANETs can be reduced; 2) compared with the road network with low traffic demand, more RSUs need to be deployed in the road network with high traffic demand to achieve the same effect; and 3) early RSU investment is more cost effective. Our method can provide a reference for the areas where RSU investments should be made and the priority of the areas.
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