Sensor virtualization for underwater event detection

Distributed event detection is a popular application in Underwater Wireless Sensor Networks (UWSNs). The Base Station (BS) collects the measurements from multiple sensor nodes, and makes a decision based on the sensors' reports. However, due to the unpredictable moving of underwater sensor nodes and interference among multiple events, it is difficult to guarantee the accuracy of event detection. In this paper, we propose a sensor virtualization approach to deal with the event detection problem in UWSNs. The final decision making at the BS will be implemented with the reports of multiple virtual sensors. Although the events may happen in a large scale, the locations where the events happen are relatively sparse in the underwater environment. Consider the sparse property of events, we employ the technique of compressive sensing to recover the original signal from the correlated sensors' measurements. Through a proper signal reconstruction, the accurate event detection can be reached with a remarkable low sensing overhead. We implement the sensor virtualization based on the compressive sensing technique. Our approach is suitable for the high dynamic topology of UWSN, and it can improve the accuracy of event detection and reduce energy consumption in UWSNs.