  The underwater acoustic channel is one of the most challenging communication
channels. Due to periodical tidal and daily climatic variation, underwater
noise is periodically fluctuating, which result in the periodical changing of
acoustic channel quality in long-term. Also, time-variant channel quality leads
to routing failure. Routing protocols with acoustic channel estimation, namely
underwater channel-aware routing protocols are recently proposed to maintain
the routing performance. However, channel estimation algorithms for these
routing protocols are mostly linear and rarely consider periodicity of acoustic
channels. In this paper, we introduce acoustic channel estimation based on
nearest neighbor regression for underwater acoustic networks. We extend nearest
neighbor regression for SNR (Signal-to-Noise Ratio) time series prediction,
providing an outstanding prediction accuracy for intricately periodical and
fluctuating received SNR time series. Moreover, we propose a quick search
algorithm and use statistical storage compression to optimize the time and
space complexity of the algorithm. In contrast with linear methods, this
algorithm significantly improves channel prediction accuracy (over three times
at most) on both simulation and sea trial data sets. With this channel
estimation method, we then propose a Depth-Based Channel-Aware Routing protocol
(DBCAR). Taking advantage of depth-greedy forwarding and channel-aware reliable
communication, DBCAR has an outstanding network performance on packet delivery
ratio, average energy consumption and average transmission delay which is
validated through extensive simulations.
