Cognitive Intelligence with Adaptive MAC Vehicular Ad-Hoc Network Optimization

Successful and timely data delivery in evolutionary Vehicular Ad-Hoc Network (VANET) for Intelligent Transportation System (ITS) are particularly challenging due to the unique characteristics of VANET, such as fast topology change and frequent disruptions. The current VANET schemes use static parameters in an ever changing VANET topology, which results in lack of adaptability. Due to the fast topology change and frequent disruptions in VANET, a cognitive and adaptive VANET Medium Access Control (MAC) scheduling optimization protocol scheme, which is able to change its transmission parameters, to suit different topology change in VANET is preferable. In the proposed Adaptive V ANET MAC, namely AdMAC, change of signal to noise and interference ratio (SINR) is proposed as an indicator to represent VANET transmission environment and traffic density. The change of SINR is then used to determine the V ANET MAC contention window size. Simulation is conducted under congested and non-congested traffics, in the urban map of Kuala Lumpur, Malaysia. Results show that the proposed AdMAC has better adaptability, in terms of packet success rate and average delay.