  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.
