Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm

  • Authors:

  • Alexandre Ciancio;Sundeep Pattem;Antonio Ortega;Bhaskar Krishnamachari

  • Affiliations:

  • University of Southern California, Los Angeles, California;University of Southern California, Los Angeles, California;University of Southern California, Los Angeles, California;University of Southern California, Los Angeles, California

  • Venue:
  • Proceedings of the 5th international conference on Information processing in sensor networks
  • Year:
  • 2006

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Abstract

We address the problem of energy consumption reduction for wireless sensor networks, where each of the sensors has limited power and acquires data that should be transmitted to a central node. The final goal is to have a reconstructed version of the data measurements at the central node, with the sensors spending as little energy as possible, for a given data reconstruction accuracy. In our scenario, sensors in the network have a choice of different coding schemes to achieve varying levels of compression. The compression algorithms considered are based on the lifting factorization of the wavelet transform, and exploit the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as data flows towards the central node. The proposed algorithm operates by first selecting a routing strategy through the network. Then, for each route, an optimal combination of data representation algorithms i.e. assignment at each node, is selected. A simple heuristic is used to determine the data representation technique to use once path merges are taken into consideration. We demonstrate that by optimizing the coding algorithm selection the overall energy consumption can be significantly reduced when compared to the case when data is just quantized and forwarded to the central node. Moreover, the proposed algorithm provides a tool to compare different routing techniques and identify those that are most efficient overall, for given node locations. We evaluate the algorithm using both a second-order autoregressive (AR) model and empirical data from a real wireless sensor network deployment.