Voronoi tessellation based multiscale data compression for sensor networks

  • Authors:
  • Z. J. Xie;Lei Wang;Y. H. Liu;H. Chen

  • Affiliations:
  • College of Information, Renmin University of China, Beijing, China;College of Software, Hunan University, Changsha, China;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX;College of Information, Renmin University of China, Beijing, China

  • Venue:
  • TELE-INFO'06 Proceedings of the 5th WSEAS international conference on Telecommunications and informatics
  • Year:
  • 2006

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Abstract

Though several wavelet-based compressing solutions for sensor network have been proposed, those algorithms can only transform the regularly sample data. In this paper, we propose a distributed wavelet-based algorithm which can transform irregularly sample data. Considering the characteristics and location information of nodes in sensor networks, a new distributed data aggregation mode DDAM based on "subnet" is proposed firstly. On the basis of these new models, a novel wavelet-based irregularly sample data compression and data transform model DDWM is proposed for sensor networks. Theoretical analyses and simulation results show that, the above new methods have the good ability of approximation, and can compress the data efficiently and can reduce the amount of data greatly, So, it can prolong the lifetime of the whole network to a greater degree.