Abstract
In this paper, we propose an algorithm for wavelet based spatio-temporal data compression in wireless sensor networks. By employing a ring topology, the algorithm is capable of supporting a broad scope of wavelets that can simultaneously explore the spatial and temporal correlations among the sensory data. Furthermore, the ring based topology is in particular effective in eliminating the “border effect” generally encountered by wavelet based schemes. We propose a “Hybrid” decomposition based wavelet transform instead of wavelet transform based on the common dyadic decomposition, since temporal compression is local and far cheaper than spatial compression in sensor networks. We show that the optimal level of wavelet transform is different due to diverse sensor network circumstances. Theoretically and experimentally, we conclude the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy consumption and delay compared to other schemes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Estrin, D., Govindan, R., Heideman, J., Kumar, S.: Next century challenges: scalable coordination in sensor networks. In: Proc. MOBICOM, Seattle, USA (August 1999)
Lindsey, S., Raghavendra, C., Sivalingam, K.: Data gathering algorithms in sensor networks using energy metrics. IEEE transactions on parallel and distributed systems 13, 924–935 (2002)
Xu, N., Rangwala, S., Chintalapudi, K., Ganesan, D., Broad, A., Govindan, R., Estrin, D.: A wireless sensor network for structuralmonitoring. In: Proc. ACM Sen Sys., Maryland, USA (November 2004)
Chen, H., Li, J., Mohapatra, P.: RACE: Time Series Compression with Rate Adaptive and Error Bound for Sensor Networks. In: Proc. MASS, Fort Lauderdale, USA (October 2004)
Ganesan, D., Estrin, D., Heidemann, J.: DIMENSIONS: Why do we need a new data handling architecture for sensor networks? SIGCOMM Comput. Commun. Rev. 33(1), 143–148 (2003)
Servetto, S.: Distributed signal processing algorithms for the sensor broadcast problem. In: Proc. CISS, Philadelphia, USA (March 2003)
Ciancio, A., Ortega, A.: A distributed wavelet compression algorithm for wireless multihop sensor networks using lifting. In: Proc. ICASSP, Philadelphia, USA (March 2005)
Acimovic, J., Cristescu, R., Lozano, B.: Efficient distributed multiresolution processing for data gathering in sensor networks. In: Proc. ICASSP, Philadelphia, USA (March 2005)
Karlsson, G., Vetterli, M.: Extension of finite length signals for subband coding. Signal processing 17, 161–168 (1989)
Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy- Efficient Communication Protocol for Wireless Microsensor Networks. In: Proc. HICSS, Hawaii, USA (January 2000)
Xu, Y., Heidemann, J., Estrin, D.: Geography-informed energy conservation for ad hoc routing. In: Proc. MobiCom, Rome, Italy (July 2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, S., Lin, Y., Wang, J., Zhang, J., Ouyang, J. (2006). Compressing Spatial and Temporal Correlated Data in Wireless Sensor Networks Based on Ring Topology. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_29
Download citation
DOI: https://doi.org/10.1007/11775300_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35225-9
Online ISBN: 978-3-540-35226-6
eBook Packages: Computer ScienceComputer Science (R0)