A scalable support vector machine for distributed classification in ad hoc sensor networks

A scalable support vector machine (SVM) is proposed for distributed classification in ad hoc wireless sensor networks (WSNs) in this paper. The main idea is to train SVM classifier using only the local dataset, and evaluate the global nonlinear classifier via a dynamic consensus algorithm with communication only between neighbors instead of among all agents (sensor node) in the network. Specifically, by introducing a sequential gradient ascent based algorithm and modifying the formulation of the bias, the training process can be executed in a distributed and parallel way without information exchange among agents. After the distributed training of SVM, each node has one set of Lagrange multipliers corresponding to the local dataset. Then we adopt the dynamic consensus algorithm to evaluate the global nonlinear classifier for each agent in the network with only information exchange between neighbors. A novel dynamic consensus formulation is introduced and its convergence is proved. What’s more, since it only exchanges information between neighbors during evaluation, the proposed algorithm is scalable for large-scale ad hoc sensor network and considerable communication energy can be reduced, which will prolong the lifetime of the whole network. Examples from the UCI repository demonstrate the effectiveness of the proposed algorithm.