A compressive sensing approach for obstacle mapping in wireless sensor networks

In this paper, a group of outdoor wireless sensors are assigned to trace the indoor barriers. We introduce a framework based on compressive sensing theory that allows sensors to map the 2D spatial details of a building non-invasively. By exploiting an alternative projection method, called fan-beam, sensors can efficiently observe area indirectly. It is also shown that how sparse representation of the spatial parameters in some domains may be utilized in order to realize obstacle mapping with minimum number of wireless transmissions and receptions. In addition, another random approach is considered and simulation results illustrate premier performance of our novel framework. Further, we test our method in different circumstances and show how weaknesses can be improved.