Realistic acoustic prediction models to efficiently design higher layer protocols in underwater wireless sensor networks

The study of Underwater Wireless Sensor Networks (UWSNs) as a research field has grown significantly in recent years offering a multitude of proposals for resolving the communication between nodes and protocols for information exchange networks. Acoustics has been used by nature for many millennia to communicate in underwater environments as a language; dolphins and whales for instance are able to use it for sending information between their groups. Wireless sensor networks have been proposed for their deployment in underwater environments where many applications, such as aquiculture, pollution monitoring, offshore exploration, etc., would benefit from this technology. Despite having a very similar functionality, Underwater Wireless Sensor Networks exhibit several architectural differences with respect to the terrestrial ones, which are mainly due to transmission medium characteristics (sea water) and the signal employed to transmit data (acoustic ultrasound signals). Then, the design of appropriate network architecture for UWSNs is seriously hardened by the conditions of the communication system and, as a consequence, what is valid for terrestrial WSNs is perhaps not valid for UWSNs. So, a general review of the overall network architecture is required in order to supply an appropriate network service for the demanding applications in such an unfriendly submarine communication environment. 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 (DPM). To facilitate large-scale system design under such conditions (e.g. power allocation), we develop a Statistical Propagation Model (SPM) in which the transmission loss is treated as a random variable. By repetitive computation of an acoustic field using a ray tracing tool (DPM) for a set of varying environmental conditions (surface height, wave activity, small displacements of a transmitter and a 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 increase, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. A statistical prediction 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 transmitting power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc.).