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The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

Localization is important for underwater sensor networks as the validity of data and the maintenance of nodes both need location information. Because of the Snell effect caused by non-uniform distribution of sound speed, the signal propagation path is curved, which would lead to errors when traditional linear propagation localization models are used. Sound speed profiles (SSPs) can be used to correct the signal trajectory error according to the ray theory, so as to improve the localization accuracy. To simplify the expression of original SSPs and reduce the computational complexity of ray tracing while guaranteeing the accuracy, we propose a stratified linear SSP simplification method with a distance-minimization-based equal-interval control points searching (DM-EICPS) algorithm. Simulation results show that, compared with other curve approximation algorithms, the DM-EICPS algorithm not only generates simplified SSPs rapidly, but also achieves good SSP approximation accuracy. Moreover, the corrected localization accuracy with simplified SSPs is an order of magnitude higher than the linear propagation model, and the time overhead of ray tracing with simplified SSPs is obviously reduced compared to original SSPs.

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