Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios.

In an underwater acoustic channel, the propagation conditions are known to vary in time, causing the deviation of the received signal strength from the nominal value predicted by a deterministic propagation model. To facilitate a large-scale system design in such conditions (e.g., power allocation), we have developed a statistical propagation model in which the transmission loss is treated as a random variable. By applying repetitive computation to the acoustic field, using ray tracing for a set of varying environmental conditions (surface height, wave activity, small node displacements around nominal locations, etc.), an ensemble of transmission losses is compiled and later used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e., to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc.).

In the last years, wireless sensor networks have been proposed for their deployment in underwater environments where a lot of applications like aquiculture, pollution monitoring and offshore exploration would benefit from this technology. Despite having a very similar functionality, Underwater Wireless Sensor Networks (UWSNs) exhibit several architectural differences with respect to the terrestrial ones, which are mainly due to the transmission medium characteristics (sea water) and the signal employed to transmit data (acoustic ultrasound signals). So, the design of appropriate network architecture for UWSNs is seriously hardened by the specific characteristics of the communication system. In this work we analyze several acoustic channel models for their use in underwater wireless sensor network architectures. For that purpose, we have implemented them by using the OPNET Modeler tool in order to perform an evaluation of their behavior under different network scenarios. Finally, some conclusions are drawn showing the impact on UWSN performance of different elements of channel model and particular specific environment conditions

Abstract-Propagation conditions in an underwater acoustic channel are known to vary in time, causing the received signal strength to deviate from the nominal value predicted by a deterministic propagation model. To facilitate large-scale system design in such conditions (e.g. power allocation), we develop a statistical propagation model in which the transmission loss is treated as a random variable. By repetitive computation of acoustic field using ray tracing for a set of varying environmental conditions (surface height, wave activity, small displacements of transmitter and receiver around nominal locations), an ensemble of transmission losses is compiled which is then used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e. to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc.).

When optimizing a wavelet image coder, the two main targets are to (1) improve its rate-distortion (R/D) performance and (2) reduce the coding times. In general, the encoding engine is mainly responsible for achieving R/D performance. It is usually more complex than the decoding part. A large number of works about R/D or complexity optimizations can be found, but only a few tackle the problem of increasing R/D performance while reducing the computational cost at the same time, like Kakadu, an optimized version of JPEG2000. In this work we propose an optimization of the E_LTW encoder with the aim to increase its R/D performance through perceptual encoding techniques and reduce the encoding time by means of a graphics processing unit-optimized version of the two-dimensional discrete wavelet transform. The results show that in both performance dimensions, our enhanced encoder achieves good results compared with Kakadu and SPIHT encoders, achieving speedups of 6 times with respect to the original E_LTW encoder.