- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Safety and efficiency applications in vehicular networks rely on the exchange of periodic messages between vehicles. These messages contain position, speed, heading, and other vital information that makes the vehicles aware of their surroundings. The drawback of exchanging periodic cooperative messages is that they generate significant channel load. Decentralized Congestion Control (DCC) algorithms have been proposed to minimize the channel load. However, while the rationale for periodic message exchange is to improve awareness, existing DCC algorithms do not use awareness as a metric for deciding when, at what power, and at what rate the periodic messages need to be sent in order to make sure all vehicles are informed. We propose an environment- and context-aware DCC algorithm combines power and rate control in order to improve cooperative awareness by adapting to both specific propagation environments (e.g., urban intersections, open highways, suburban roads) as well as application requirements (e.g., different target cooperative awareness range). Studying various operational conditions (e.g., speed, direction, and application requirement), ECPR adjusts the transmit power of the messages in order to reach the desired awareness ratio at the target distance while at the same time controlling the channel load using an adaptive rate control algorithm. By performing extensive simulations, including realistic propagation as well as environment modeling and realistic vehicle operational environments (varying demand on both awareness range and rate), we show that ECPR can increase awareness by 20% while keeping the channel load and interference at almost the same level. When permitted by the awareness requirements, ECPR can improve the average message rate by 18% compared to algorithms that perform rate adaptation only.
In this paper, we proposed a combined rate and power DCC algorithm that efficiently achieves the target awareness and rate requirements given by the application context (e.g., target applications, vehicle speed, traffic density) in varying propagation environments. By using path loss exponent estimation, ECPR adapts the transmit power to reach the target awareness range. ECPR controls the channel load by adjusting the rate and power according to the current channel load, awareness range, and rate information. We perform realistic simulations, incorporating real world information about mobile and static objects (vehicles, buildings, and foliage) and test ECPR in scenarios with varying LOS conditions, highly dynamic network topology, and different environments (highway and urban). We show that ECPR has the ability to obtain higher rate when the awareness requirements allow it, improving the average rate by 15+%, while keeping the target awareness and channel load. If the awareness requirements are more stringent or the propagation environment more harsh, ECPR efficiently trades rate to improve the awareness by up to 20 percentage points. ECPR can be implemented atop existing DCC solutions with little effort, as the only additional information it requires is the transmit power of the message that can be piggybacked in the message itself.