Abstract
Sub-Nyquist sampling techniques for Wireless Sensor Networks (WSN) are gaining increasing attention as an alternative method to capture natural events with desired quality while minimizing the number of active sensor nodes. Among those techniques, Compressive Sensing (CS) approaches are of special interest, because of their mathematically concrete foundations and efficient implementations. We describe how the geometrical representation of the sampling problem can influence the effectiveness and efficiency of CS algorithms. In this paper we introduce a Map-based model which exploits redundancy attributes of signals recorded from natural events to achieve an optimal representation of the signal.
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References
Bajwa, W., Haupt, J., Sayeed, A., Nowak, R.: Compressive wireless sensing. In: Int. Conf. on Information Processing in Sensor Networks, IPSN (2006)
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Networks 38 (2002)
Raghavendra, C.S., Sivalingam, K.M., Znati, T. (eds.): Wireless Sensor Networks, 2nd edn. (2005) ISBN: 978-1-4020-7883-5
Candès, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2) (2008)
Candès, E.: Compressive sampling. Int. Congress of Mathematics, Madrid, Spain (2006)
Baraniuk, R.: Compressive sensing. IEEE Signal Processing Magazine 24(4) (2007)
Donoho, D.: Compressed sensing. IEEE Trans. on Information Theory 52(4) (2006)
Khelil, A., Shaikh, F.K., Ayari, B., Suri, N.: MWM: A Map-based World Model for Event-driven Wireless Sensor Networks. In: Proc. of The 2nd ACM International Conference on Autonomic Computing and Communication Systems, AUTONOMICS (2008)
Khelil, A., Shaikh, F.K., Ali, A., Suri, N.: gMAP: An Efficient Construction of Global Maps for Mobility- Assisted Wireless Sensor Networks. In: The Sixth Annual Conference on Wireless On demand Network Systems and Services, WONS (2009)
Candès, E., Romberg, J.: Sparsity and incoherence in compressive sampling. Inverse Problems 23(3) (2007) ISSN 0266-5611
Zahedi, S., Bisdikian, C.: A framework for QoI-inspired analysis for sensor network deployment planning. In: 2nd Int’l Workshop on Performance Control in Wireless Sensor Networks, PWSN (2007)
Sundararaman, B., et al.: Clock Synchronization for Wireless Sensor Networks: A Survey. Ad-Hoc Networks 3(3) (May 2005)
Cevher, V., Gurbuz, A.C., McClellan, J.H., Chellappa, R.: Compressive wireless arrays for bearing estimation of sparse sources in angle domain. In: ICASSP 2008 (2008)
Hern, B.: Robustness of Compressed Sensing in Sensor Networks, Bachelore thesis (2008)
Kimura, N., Latifi, S.: A Survey on Data Compression in Wireless Sensor Networks. In: Proceedings of the international Conference on information Technology: Coding and Computing (ITCC), vol. II, pp. 8–13. IEEE Computer Society, Los Alamitos (2005)
Barr, K., Asanovi, K.: Energy aware lossless data compression. In: Proceedings of the 1st international Conference on Mobile Systems, Applications and Services (MobiSys), pp. 231–244. ACM Press, New York (2003)
Kusuma, J., Doherty, L., Ramchandran, K.: Distributed compression for sensor networks. In: Proc. International Conf. Image Processing (ICIP), October 2001, vol. 1, pp. 82–85 (2001)
Arici, T., Gedik, B., Altunbasak, Y., Liu, L.: PINCO: a Pipelined In-Network Compression Scheme for Data Collection in Wireless Sensor Networks. In: Proceedings of 12th International Conference on Computer Communications and Networks (October 2003)
Adler, M.: Collecting correlated information from a sensor network. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, SODA (2005)
Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: Proceedings of the International Conference on Data Engineering, ICDE (2006)
Pattem, S., Krishnamachari, B., Govindan, R.: The impact of spatial correlation on routing with compression in wireless sensor networks. In: Proceedings of the International Conference on Information Processing in Sensor Networks, IPSN (2004)
Pradhan, S., Ramchandran, K.: Distributed source coding using syndromes (DISCUS): Design and construction. IEEE Transactions on Information Theory 49(3) (2003)
Silberstein, A., Puggioni, G., Gelfand, A., Munagala, K., Yang, J.: Making Sense of Suppressions and Failures in Sensor Data: A Bayesian Approach. In: Proceedings of the International Conference on Very Large Data Bases, VLDB (2007)
Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4) (1973)
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Mahmudimanesh, M., Khelil, A., Yazdani, N. (2009). Map-Based Compressive Sensing Model for Wireless Sensor Network Architecture, A Starting Point. In: Hesselman, C., Giannelli, C. (eds) Mobile Wireless Middleware, Operating Systems, and Applications - Workshops. MOBILWARE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03569-2_8
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DOI: https://doi.org/10.1007/978-3-642-03569-2_8
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