International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics

ALGOSENSORS 2009: Algorithmic Aspects of Wireless Sensor Networks pp 191-202

Compressing Kinetic Data from Sensor Networks

  • Sorelle A. Friedler
  • David M. Mount
Conference paper

DOI: 10.1007/978-3-642-05434-1_20

Volume 5804 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Friedler S.A., Mount D.M. (2009) Compressing Kinetic Data from Sensor Networks. In: Dolev S. (eds) Algorithmic Aspects of Wireless Sensor Networks. ALGOSENSORS 2009. Lecture Notes in Computer Science, vol 5804. Springer, Berlin, Heidelberg

Abstract

We introduce a framework for storing and processing kinetic data observed by sensor networks. These sensor networks generate vast quantities of data, which motivates a significant need for data compression. We are given a set of sensors, each of which continuously monitors some region of space. We are interested in the kinetic data generated by a finite set of objects moving through space, as observed by these sensors. Our model relies purely on sensor observations; it allows points to move freely and requires no advance notification of motion plans. Sensor outputs are represented as random processes, where nearby sensors may be statistically dependent. We model the local nature of sensor networks by assuming that two sensor outputs are statistically dependent only if the two sensors are among the k nearest neighbors of each other. We present an algorithm for the lossless compression of the data produced by the network. We show that, under the statistical dependence and locality assumptions of our framework, asymptotically this compression algorithm encodes the data to within a constant factor of the information-theoretic lower bound optimum dictated by the joint entropy of the system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sorelle A. Friedler
    • 1
  • David M. Mount
    • 1
  1. 1.Dept. of Computer ScienceUniversity of MarylandUSA