Abstract : Energy-Efficient Distributed Support Vector Machines for Wireless Sensor Networks

As the research field of mobile computing and communication advances, so does the need for a distributed, ad-hoc wireless network of hundreds to thousands of microsensors, which can be randomly scattered in the area of interest. In this paper, we present two energy-efficient algorithms to perform distributed incremental learning for the training of a Support Vector Machine (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.