Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks

Abstract Underwater acoustic networks (UANs) are attracting interest in recent decades. The unique characteristics of the underwater acoustic channel, such as long propagation delay, delay variance, and high bit error rate, present challenges for the medium access control (MAC) protocol design in UANs. Most existing medium access control protocols ignore the delay variance which prevents the accurate estimation of round trip time (RTT). The expected RTT value can be used to compute the Retransmission Time-Out (RTO) or the waiting time in MAC. The estimation of RTT is also meaningful for Automatic Repeat re-Quest (ARQ) scheme because the system should ensure reliable data transmissions in the presence of high bit error rate in the underwater acoustic channel. By analyzing the impact of RTO on throughput under the effect of delay variance, we conclude that the fixed RTO is inefficient and RTO should be adaptively set to improve the throughput. We present a novel approach of predicting the RTT using a Bayesian dynamic linear model, and then adjust RTO adaptively according to the predicted values. Simulation results show that the predicted values can adapt quickly to the sample RTT values. Under the effect of RTT fluctuations, the Bayesian algorithm offers performance gains in terms of throughput and prediction performance, comparing with Karn’s algorithm. Our study highlights the value of predicting the RTT using Bayesian approach in underwater acoustic networks.