RSU-PN-ALL

Citation Author(s):
Guangtong
Hu
Submitted by:
Guangtong Hu
Last updated:
Tue, 08/20/2024 - 11:37
DOI:
10.21227/kbnp-9212
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Abstract 

For optimizing RSU deployment, comprehensive data is essential. Geographical data includes detailed road network topology, traffic flow patterns, and geographic coordinates. Communication range data covers effective ranges of RSUs and connected vehicles, considering signal strength and environmental conditions. Vehicle data involves classifications based on communication capabilities and percentages of connected vehicles. Network performance data includes latency requirements, packet loss rates, and bandwidth needs. Environmental data assesses the impact of weather and physical obstacles on communication. Deployment constraints consider infrastructure costs, power supply availability, and regulatory compliance. Simulation data generates traffic scenarios for evaluating system performance. Optimization parameters define criteria for evaluating effectiveness, such as coverage and cost. Algorithmic considerations involve starting points, convergence criteria, and performance metrics. Collecting and analyzing these data enables the development of a robust model for optimized RSU deployment.

Instructions: 

For optimizing RSU deployment, comprehensive data is essential. Geographical data includes detailed road network topology, traffic flow patterns, and geographic coordinates. Communication range data covers effective ranges of RSUs and connected vehicles, considering signal strength and environmental conditions. Vehicle data involves classifications based on communication capabilities and percentages of connected vehicles. Network performance data includes latency requirements, packet loss rates, and bandwidth needs. Environmental data assesses the impact of weather and physical obstacles on communication. Deployment constraints consider infrastructure costs, power supply availability, and regulatory compliance. Simulation data generates traffic scenarios for evaluating system performance. Optimization parameters define criteria for evaluating effectiveness, such as coverage and cost. Algorithmic considerations involve starting points, convergence criteria, and performance metrics. Collecting and analyzing these data enables the development of a robust model for optimized RSU deployment.