Abstract: This paper proposes a two-tier structure for improving the MAC layer performance of a vehicular network. In tier-1, an improved vehicle-ID based analytical model is proposed. In tier-2, a fuzzy-based computational system named FUZZ-CCS is designed for controlling congestion in the vehicular network. Initially, the paper considers a fundamental element of the vehicular network, namely that each CAM generated by a vehicle is associated with a unique ID. Using this property, every vehicle weighs the random backoff number chosen by them in the back-off process, with the vehicle ID incorporated in their respective CAMs, eventually leading to the selection of a distinctive random back-off number. The vehicle ID based Markov model is validated through MATLAB. Further, in tier-2, collision probability (obtained from the proposed analytical model) and vehicular density are considered as input for designing the FUZZ-CCS. CAM broadcast rate is considered as the adapting parameter for controlling congestion throughout the network. Obtained simulation results show that the proposed FUZZ-CCS outperforms the fixed CAM rate IEEE 802.11p in terms of collision probability.
Keywords: FUZZ-CCS, congestion control, vehicle-ID, random back-off number, contention window, collision probability
