Tracking and Exploiting Correlations in Dense Sensor Networks

Dragan Petrovic and Jim Chou
(Professor Kannan Ramchandran)
(DARPA) F30602-00-2-0538, (DARPA) F29601-99-1-0169, and (NSF) CCR-0219722

We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithms. While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently, in this project, we propose an orthogonal approach to previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is low in complexity and easy to implement, while an adaptive filtering framework is used to continuously learn the relevant correlation structures in the sensor data. Our simulations show the power of our proposed algorithms, revealing their potential to effect significant energy savings (from 15%-40%) for typical sensor data corresponding to a multitude of sensor modalities.


Figure 1: Distributed source coding setup: node with reading X must compress the reading to close to H(X|Y) without knowing Y

Figure 2: Many sensor nodes reporting to a data-gathering node are clustered according to the correlations of their readings.

[1]
J. Chou, D. Petrovic, and K. Ramchandran, "Tracking and Exploiting Correlations in Dense Sensor Networks," Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, November 2002.

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