Training a SVM-based classifier in distributed sensor networks

The emergence of smart low-power devices (motes), which have micro-sensing, on-board processing, and wireless communication 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 analytical studies and simulation experiments, we show that the two proposed algorithms exhibit similar performance to the traditional centralized SVM training methods, while being much more efficient in terms of energy cost.