Density-Aware BEB Optimization Using SCT for Emergency Alerts in Vehicular Networks

Real-time sharing of traffic information is crucial for effectively reducing losses after road traffic accidents and disasters. However, traditional methods for collecting and sharing this information are often inefficient, consume considerable resources, and face persistent challenges regarding cost and real- time performance. These issues hinder the ability to achieve real-time sharing of large-scale traffic information. To tackle this problem, this paper presents a new data storage method called the storage counting tree (SCT). Unlike traditional storage methods, where the amount of data is closely tied to the storage space required, the counting tree reuses tree nodes, making its storage space usage independent of the data scale. In this context, the coordinates information of vehicles can be shared efficiently via SCT and thus the density can be evaluated. Accordingly, we propose to optimize the backoff window of the traditional binary exponential backoff (BEB) algorithm based on the density. The experiments show that a 127-byte SCT can accommodate up to 4.2 billion entries, demonstrating impressive storage efficiency. Moreover, the system significantly reduces latency for critical message transmission and shows improved adaptability across various scenarios.