Adaptive backoff scheme for contention-based vehicular networks using fuzzy logic

In contention-based wireless networks, collisions between data packets can be reduced by introducing a random delay before each transmission. Backoff schemes are those that provide the backoff interval from which the random delay is drawn. In this paper, we propose a new scheme which calculates the backoff interval dynamically according to the network conditions. The network conditions are measured locally by each node, which supports the distributed nature of the vehicular networks. The measures are used by a fuzzy inference system to calculate the backoff interval. We compare the proposed scheme with other known schemes: the binary exponential backoff (BEB), the sensing backoff algorithm (SBA) and an optimal scheme which requires the knowledge of the number of nodes in the network (Genie). The evaluation measures are the throughput and fairness. Results show an improvement of the fuzzy-based schemes compared to the BEB and SBA, especially for large number of nodes in the network.