A Localized Adaptive Strategy to Calculate the Backoff Interval in Contention-Based Vehicular Networks

The dynamic nature of vehicular networks with their fast changing topology poses several challenges to setup communication between vehicles. Packet collisions are considered to be the main source of data loss in contention-based vehicular networks. Retransmission of collided packets is done several times until an acknowledgment of successful reception is received or the maximum number of retries is reached. The retransmission delay is drawn randomly from an interval, called the backoff interval. A good choice of the backoff interval reduces the number of collisions and the waiting periods of data packets, which increases the throughput and decreases the energy consumption. An optimal backoff interval could be obtained if global network information spread in the network in a short time. However, this is practically not achievable which motivates the efficient utilization of local information to approach the optimal performance. In this paper, we propose a localized adaptive strategy that calculates the backoff interval for unicast applications in vehicular networks. The new strategy uses fuzzy logic to adapt the backoff interval to the fast changing vehicular environment using only local information. We present four schemes of that strategy that differ in their behavior and the number of inputs. We compare the proposed schemes with other known schemes, binary exponential backoff, backoff algorithm, and an optimal scheme, in terms of throughput, fairness, and energy consumption. Results show that by proper tuning of the fuzzy parameters and rules, one of the proposed schemes outperform the other schemes, and approach the optimal results.