Compressive detection and localization of multiple heterogeneous events in sensor networks

This paper focuses on the comprehensive event detection and localization problem which efficiently detects not only the number and the position, but also the event signal strength of events in sensor networks. We consider the practical situation where multiple events may simultaneously occur, their signal with heterogeneous strength attenuates over distance and their signal propagation region may overlap. The problem becomes even more challenging when we get rid of the commonly made impractical assumptions, such as the oversimplified binary detection model, the awareness of the number and potential positions of future events, and the existing of super sensor nodes with unlimited sensing range. Inspired by spatially sparse event occurrences, we propose the efficient compressive sensing based approach called CED. Instead of collecting complete sensor readings, our self-driven and fully distributed measurement construction process makes only a small number of qualified measurements, enabling compressive sensing based data recovery. The distinguishing feature of our approach is that it requires no knowledge of, and is adaptive to, the number of occurred events which is changing over time. We have validated signal attenuation model of real-world events and implemented the proposed approach on a testbed of 36 TelosB motes. Testbed experiments and simulation results jointly demonstrate that our approach can achieve high detection rate with event occurred grids while incurring modest transmission overhead.