TY - JOUR
T1 - An Altruistic Prediction-Based Congestion Control for Strict Beaconing Requirements in Urban VANETs
AU - Zemouri, Sofiane
AU - Djahel, Soufiene
AU - Murphy, John
N1 - Funding Information:
Manuscript received March 13, 2017; revised July 27, 2017; accepted September 26, 2017. Date of publication January 25, 2018; date of current version November 19, 2019. This work was supported in part by the Science Foundation Ireland under Grant 10/CE/I1855, in part by the Science Foundation Ireland through the Irish Software Engineering Research Centre under Grant 13/RC/2094, in part by the Earth and Natural Sciences Doctoral Studies Programme through the Higher Education Authority under the Programme for Research in Third-Level Institutions Cycle 5, and in part by the European Regional Development Fund. This paper was recommended by Associate Editor D. Akopian. (Corresponding author: Sofiane Zemouri.) S. Zemouri and J. Murphy are with the Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Periodic beacon messages are one of the building blocks that enable the operation of vehicular ad hoc networks (VANETs) applications. In vehicular networks environments, congestion and awareness control mechanisms are key for a reliable and efficient functioning of vehicular applications. In order to control the channel load, a reliable mechanism allowing real-time measurements of parameters like the local density of vehicles is a must. These measurements can then serve as an input to perform a fast adaptation of the transmit parameters. In this paper, considerable efforts have been directed in the recent years toward designing flexible yet robust protocols solving this problem; yet, very few have considered a proactive adaptation of the transmit parameters as a preventive measure from channel load peaks. To this end, we take the opportunity to introduce prediction and adaptation algorithm (PA-A), a new congestion control protocol that performs a joint adaptation of the transmit rate and power, relying on an altruistic short-term prediction algorithm that estimates the vehicular density around a given vehicle within the next short while. Additionally, PA-A adapts the transmit parameters in a way that guarantees the strict beaconing requirements and satisfies the level of awareness required for the operation of most critical VANET applications. The results of the simulations performed in a realistic scenario justify our theoretical considerations and confirm the efficiency and the effectiveness of our protocol by showing significant improvements in terms of network performance (up to 8% and 14% improvement in collision rate; and up to 10% and 20% increase in busy ratio compared to our previous scheme and the ETSI schemes, respectively) as well as the achieved level of awareness (higher coverage with higher transmission rate and power in dense scenarios, and up to 8% and 55% improvement in density perception accuracy compared to our previous scheme and the ETSI schemes, respectively).
AB - Periodic beacon messages are one of the building blocks that enable the operation of vehicular ad hoc networks (VANETs) applications. In vehicular networks environments, congestion and awareness control mechanisms are key for a reliable and efficient functioning of vehicular applications. In order to control the channel load, a reliable mechanism allowing real-time measurements of parameters like the local density of vehicles is a must. These measurements can then serve as an input to perform a fast adaptation of the transmit parameters. In this paper, considerable efforts have been directed in the recent years toward designing flexible yet robust protocols solving this problem; yet, very few have considered a proactive adaptation of the transmit parameters as a preventive measure from channel load peaks. To this end, we take the opportunity to introduce prediction and adaptation algorithm (PA-A), a new congestion control protocol that performs a joint adaptation of the transmit rate and power, relying on an altruistic short-term prediction algorithm that estimates the vehicular density around a given vehicle within the next short while. Additionally, PA-A adapts the transmit parameters in a way that guarantees the strict beaconing requirements and satisfies the level of awareness required for the operation of most critical VANET applications. The results of the simulations performed in a realistic scenario justify our theoretical considerations and confirm the efficiency and the effectiveness of our protocol by showing significant improvements in terms of network performance (up to 8% and 14% improvement in collision rate; and up to 10% and 20% increase in busy ratio compared to our previous scheme and the ETSI schemes, respectively) as well as the achieved level of awareness (higher coverage with higher transmission rate and power in dense scenarios, and up to 8% and 55% improvement in density perception accuracy compared to our previous scheme and the ETSI schemes, respectively).
KW - Adaptive beaconing
KW - density forecasting
KW - density prediction
KW - intelligent transportation systems (ITS)
KW - transmission power control
KW - transmission rate control
KW - vehicular ad hoc networks (VANETs)
UR - http://www.scopus.com/inward/record.url?scp=85040993942&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2017.2759341
DO - 10.1109/TSMC.2017.2759341
M3 - Article
AN - SCOPUS:85040993942
VL - 49
SP - 2582
EP - 2597
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
SN - 2168-2216
IS - 12
M1 - 8269413
ER -
