Training a SVMbased classifier in distributed sensor networks

The emergence of smart low-power devices (motes), which have micro-sensing, on-board processing, and wireless com-munication capabilities, has impelled research in distributed and on-line learning under communication constraints. In this paper, we show how to perform a classification task in a wireless sensor network using distributed algorithms for Support Vector Machines (SVMs), taking advantage of the sparse representation that SVMs provide for the decision boundaries. We present two energy-efficient algorithms that involve a distributed incremental learning for the training of a SVM in a wireless sensor network, both for stationary and non-stationary sample data (concept drift). Through analyti-cal studies and simulation experiments, we show that the two proposed algorithms exhibit similar performance to the tradi-tional centralized SVM training methods, while being much more efficient in terms of energy cost. 1