Ocean Monitoring Framework based on Compressive Sensing using Acoustic Sensor Networks

This paper presents a framework for spatio-temporal monitoring of ocean environment using large-scale underwater acoustic sensor networks (UWASNs). Our goal is to exploit low-cost, battery-operated technology for acoustic communication to enable long-term, mass deployment of UWASNs for a wide range of monitoring applications in need of high spatio-temporal sampling rate and near real-time data delivery. Inspired by theory of compressive sensing (CS), the framework supports opportunistic random deployment of sensor nodes and relies on random channel access to harvest their data and construct spatio-temporal fields of the underlying sensed phenomena. In order to save bandwidth and energy, we consider a positioning scheme in which the sensor nodes remain silent and just listen for beacon signals from few reference nodes to localize themselves. After this initial localization phase, the sensing process begins. At regular intervals (frames), a set of random sensors sample their transducers and independently try to transmit their measurements to a fusion center (FC) for CS-based field reconstruction. Due to this random access of the acoustic channel, some of the packets may collide at the FC, wasting both energy and bandwidth. For slowly varying fields, consecutive frames have high correlations. We exploit this information during the field reconstruction, and show by simulation results that the number of sensors participating in each frame can be reduced drastically. This decreases the number of collisions at the FC, thus saving energy and prolonging the life-time of the network.