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Distributed Compressed Sensing (DCS) and its Application in Wireless Sensor Network (WSN)

Ghasimi, Mohsen | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45869 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Babaei Zadeh, Masoud
  7. Abstract:
  8. Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology, wireless communications,and digital electronics have enabled the development of low-cost, low-power,multifunctional sensor nodes that are small in size and communicate untethered in short distances.Wireless Sensor Network (WSN) including these sensors has been utilized in many applications, namely military and environmental applications. Due to unchangeable battery in most WSNs, Power consumption is an important challenge in such systems. A sensor node expends maximum energy in data communication. Control environmental parameter is among the most important applications of WSN. The actual physical environmental information such as temperature and humidity is compressible owning to the presence of temporal correlation and spatial correlation, thus using compressive sensing theory is an appropriate choice. In this thesis, we intend to reconstruct signals from its measurements by using distributed compressed sensing and the location of sensors. By this method, it is possible to reconstruct common support signals whose sparsity is K, with M>K measurments per sensor which is impossible via distributed compressed sensing.For reconstructing common support signals, we decompose our algorithm in two seprate parts, namely finding support signals and estimating the coefficients. We prove that Trivial Pursuit algorithm is an optimum algorithm among all one-step greedy algorithms. Furthermore, we propose three methods based on Recursive Least square, Kalman filtering and Laplacian. The key contribution of our method is that the minimum number of measurments for proper reconsruction of temprature is 10 times fewer than the number of measurments required in previous methods
  9. Keywords:
  10. Wireless Sensor Network ; Compressive Sensing ; Distributed Compressed Sensing (DCS)

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