In this work, we consider the problem of designing adaptivedistributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correl...
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ISBN:
(纸本)0780393058
In this work, we consider the problem of designing adaptivedistributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributedalgorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributedalgorithms that perform network division into groups of dif ferent sizes. The distribution of the group sizes within the network is the result of an optimal trade-off between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing.
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