Distributed real-time sound speed profiling in underwater environments

Acoustic Underwater Networks (AUNs) operate in a dynamic and inhomogeneous medium where acoustic signals tend to refract causing difficulty in sustaining connectivity and locating lost nodes. Such inhomogeneity is usually apprehended by observing sound speed variations in aquatic mediums, which is obtained by taking measurements at discrete depths, using historical samples, or by applying ocean acoustic tomography. However, these techniques do not capture the medium dynamicity or pose constraints that are often difficult to achieve in such a habitat. To overcome these shortcomings, we proposes a Distributed Real-time Oceanic Profiling approach (DROP) that relies on few randomly-deployed unsynchronized nodes to estimate underwater sound speed profile (SSP). We first present a method to map the 3D environment into an equivalent 2D one. Using the 2D map, DROP uses a second order degree polynomial to estimate the trajectory of a transmitted signal and exploits the slope gradient of the polynomial in generating a layered medium. DROP then combines the measured local sound speed values and the signal refraction to predict sound speed in distinct layers and obtains an SSP. We validate DROP through simulation and show that its performance is consistent with actual measured data.