A weight-based backoff algorithm for dynamic super dense wireless sensor networks

Estimation-based backoff algorithms for channel access are being widely studied to solve the wireless channel accessing problem especially in super dense wireless networks. In such algorithms, the accuracy of the channel state estimation seriously determines the performance. How to make the accurate estimation in an efficient way to meet the system requirements is essential in designing new channel access algorithms. In this paper, we study the confidence level of the information about the channel state which the sampled channel parameters can provide and propose a weight-based backoff algorithm in which a weight system is constructed to assign weights to different sample values according to such confidence levels to improve the estimation accuracy. Simulation results show that owing to the improved estimation accuracy, our proposed algorithm not only can provide stable throughput and fairness performance in networks with different densities, but also can achieve excellent contention window tuning speed in ever-changing networks.