Optimizing a class of in-network processing applications in networked sensor systems

A key application of networked sensor systems is to detect and classify events of interest in an environment. Such applications require processing of raw data and the fusion of individual decisions. In-network processing of the sensed data has been shown to be more energy efficient than the centralized scheme that gathers all the raw data to a (powerful) base station for further processing. We formulate the problem as a special class of flow optimization problem. We propose a decentralized adaptive algorithm to maximize the throughput of a class of in-network processing applications. This algorithm is further implemented as a decentralized in-network processing protocol that adapts to any changes in link bandwidths and node processing capabilities. Simulations show that the proposed in-network processing protocol achieves up to 95% of the optimal system throughput. We also show that path based greedy heuristics have very poor performance in the worst case.

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