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 Compre ssed 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 captur ing and analyzing RF signals transmitted between sensor nod es. On the other hand, CS provides efficient methods to an alyze this information. Our approach involves gathering characteristics abo ut the monitored environment through the wireless sensor n des 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 detai ls of how emergency situations can be monitored using our tec hnique, and will discuss the major benefits our technique p rovides over other techniques such as video monitoring. Sim ulations will show how the technique detects different asset s, and will examine the performance parameters such as noise le vel and available RF signals. Finally, the paper demonstrat es how our approach can lengthen valuable network battery life during emergencies.