Student: Mohammad A. Kanso, M.Eng Student
Supervisor: Prof. Michael Rabbat
Abstract: Radio Frequency (RF) tomography refers to the process of inferring information about contents of an environment via capturing and analyzing transmitted RF signals. Received Signal Strength (RSS) measurements acquired by the sensor nodes are analyzed to determine the location of certain obstructions in the environment. Therefore, a wireless sensor network can employ RF tomography for surveillance and monitoring in a environment. In this work, we introduce Compressed RF Tomography for monitoring via wireless sensor nodes, which requires less RSS measurements for monitoring of an environment and extends network lifetime. Compressed sensing is a recent field of research that has captured considerable attention in engineering due to its efficiency in signal sampling. Combined with RF tomography, it introduces a new approach to monitoring in wireless sensor networks. Our main contributions in this work include employing compressive sensing techniques in RF tomographic imaging, demonstrating their capabilities in centralized schemes. We also introduce decentralized schemes for in-network data analysis. Simulations throughout the work illustrate the performance into how well the approaches perform. Some real sensor network data was also used to compare our approaches with the existing approach.
[Full Description] [Paper 1 (pdf format)] [Paper 2 (pdf format)]
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