Efficient Detection and Localization of Assets in Emergency Situations

Environment monitoring is a vital tool in emergency situations, as it allows directing evacuation strategies and attempts to avoid injuries. In this paper, we present Compressed Sensing (CS) applied to Radio Frequency (RF) Tomography in a wireless sensor network as a new approach to track assets in emergencies and disasters. RF tomography refers to the inferring of information about an environment via capturing and analyzing RF signals transmitted between sensor nodes. On the other hand, CS provides efficient methods to analyze this information. Our approach involves gathering characteristics about the monitored environment through the wireless sensor nodes deployed around the area. Assuming few assets exist in the environment, they can be detected and located using information from those nodes. The paper will discuss details of how emergency situations can be monitored using our technique, and will discuss the major benefits our technique provides over other techniques such as video monitoring. Simulations will show how the technique detects different assets, and will examine the performance parameters such as noise level and available RF signals. Finally, the paper demonstrates how our approach can lengthen valuable network battery life during emergencies.