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Data Gathering with Tunable Compression in Sensor Networks
February 2008 (vol. 19 no. 2)
pp. 276-287
We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computation-intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective tradeoffs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constantfactor approximation for the grid topology and good average performance on the general graphs. Although theoretically, a more complicated randomized algorithm offers a poly-logarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.

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Citation:
Yang Yu, Bhaskar Krishnamachari, Viktor K. Prasanna, "Data Gathering with Tunable Compression in Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 2, pp. 276-287, Feb. 2008, doi:10.1109/TPDS.2007.70709
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