# Case-study of Localization via WSN Using Distributed Compressed Sensing

## Veronika Olešnaníková, Michal Kochláň, Róbert Žalman

DOI: http://dx.doi.org/10.15439/2016F567

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 1093–1096 (2016)

Abstract. Distributed compressed sensing task can be parallelized into several nodes that is highly suitable for using in Wireless Sensor Networks. Localization is one of the critical tasks solved in wireless systems. This paper investigates the possibilities of localization using compressed sensing implemented on wireless nodes and aggregation node. The presented case study simulates the application scenario of a target deployed in the field. This target is being localized by the wireless sensor network based on the emitted acoustic signal. Several types of the emitted signals have been used during the simulation runs. The emphasis was put on the properties of the reconstruction process such as compression ratio and minimization of the reconstruction error.

### References

- L. M. Kaplan, Q. Le, and P. Molnar, "Maximum likelihood methods for bearings-only target localization", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 3001 – 3004, Salt Lake City, Utah, USA, May 2001.
- Y. Weng, W. Xiao, and L. Xie, "Total least squares method for robust source localization in sensor networks using TDOA measurements", International Journal of Distributed Sensor Networks, vol. 2011, Article ID 172902, 8 pages, 2011.
- X. Qu and L. Xie, "Source localization by TDOA with random sensor position errors-part I: static sensors", in Proceedings of the 15th International Conference on Information Fusion, pp. 48-53, Singapore, July 2012.
- X. Qu and L. Xie, "Source localization by TDOA with random sensor position errors-part II: mobile sensors", in Proceedings of the 15th International Conference on Information Fusion, pp. 54-59, Singapore, July 2012.
- K. C. Ho, "Bias reduction for an explicit solution of source localization using TDOA", IEEE Transactions on Signal Processing, vol. 60, no. 5, pp. 2101-2114, 2012.
- D. Blatt and A. O. Hero, "Energy-based sensor network source localization via projection onto convex sets", IEEE Transactions on Signal Processing, vol. 54, no. 9, pp. 3614-3619, 2006.
- K. Deng and Z. Liu, "Weighted least-squares solutions of energy-based collaborative source localization using acoustic array", International Journal of Computer Science and Network Security, vol. 7, no. 1, pp. 159-165, 2007.
- Q. Shi and C. He, "A new incremental optimization algorithm for ML-based source localization in sensor networks", IEEE Signal Processing Letters, vol. 15, pp. 45-48, 2008.
- M. G. Rabbat, R. D. Nowak, and J. Bucklew, "Robust decentralized source localization via averaging", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’05), vol. 5, pp. V1057-V1060, Philadelphia, Pa, USA, March 2005.
- D. Ampeliotis and K. Berberidis, "Energy-based modelindependent source localization in wireless sensor networks," in Proceedings of the 16th European Signal Processing Conference, Lausanne, Switzerland, August 2008.
- I. F. Akyildiz and W. Su and Y. Sankarasubramaniam and E. Cayirc, "Wireless sensor networks: a survey", in Computer Networks, 2002, ISSN: 1389-1286
- A. Mainwaring and D. Culler and J. Polastre and R. Szewczyk and J. Anderson, "Wireless sensor networks for habitat monitoring", in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications,2002
- M. A. Razzaque and Ch. Bleakley and S. Dobson, "Compression in wireless sensor networks: A survey and comparative evaluation", in ACM Transactions on Sensor Networks (TOSN), 2013
- C. Caione and D. Brunelli and L. Benini, "Distributed compressive sampling for lifetime optimization in dense wireless sensor networks", in Industrial Informatics, IEEE Transactions on, 2012
- K. Hayashi and M. Nagahara and T. Tanaka, "A user’s guide to compressed sensing for communications systems", in IEICE transactions on communications, 2013
- S. Foucart and H. Rauhut, "An Invitation to Compressive Sensing", 2013
- Y. C. Eldar and G. Kutyniok, "Compressed sensing: theory and applications", 2012
- M. Fornasier and H. Rauhut, "Handbook of mathematical methods in imaging, Compressive sensing", p. 187 - 228, 2011, ISBN 978-0-387-92920-0
- M. Fornasier and H. Rauhut, "Compressive sensing", 2011, ISBN: 9780387929200
- M. Elad, "Sparse and redundant representations: from theory to applications in signal and image processing", 2010, ISBN: 9781441970107
- A. C. Fannjiang and T. Strohmer and P. Yan, "Compressed remote sensing of sparse objects",2010
- E. J. Candes and M. B. Wakin, "An Introduction to Compressive Sampling" ISSN: 1053-5888
- A. Cohen and W. Dahmen and R. DeVore, "Compressed Sensing and Best k-term Approximation", 2009