A Distributed and Adaptive Signal Processing Approach to Reducing Energy Consumption in Sensnor Networks

Jim Chou, Dragan Petrovic, Kannan Ramchandran
IEEE Infocom, San Francisco, CA, March 30 - April 3, 2003.

We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm 1. While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [1,2], in this paper, 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 extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit 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 10% - 65%) for typical sensor data corresponding to a multitude of sensor modalities.