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 control 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 that the hard-to-reach vehicles are informed. We propose ECPR, an environment and context-aware DCC algorithm , which combines power and rate control  to improve cooperative awareness by adapting to both specific propagation environments ( such as urban intersections, open highways, suburban roads) as well as application requirements (e.g., different target cooperative awareness range). Using the current context that the vehicle operates in (e.g., speed, direction, and application requirement), ECPR adjusts the transmit power of the messages  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 and environment modeling and realistic vehicle contexts (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  the awareness requirements allow , ECPR can improve the average message rate by 18\% compared to algorithms that perform rate adaptation only.
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.
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