Distributed regression over sensor networks: An support vector machine approach

This paper presents a distributed support vector regression (SV R) algorithm for sensor networks. The idea behind this algorithm is to make use of the structure similarity between sensor networks and SV Rs with 2D input data in order to implement SV R in a distributed way. During training stage, each sensor node provides its 2D coordinates as an input pattern and a sensory data as an output to the algorithm. By using local wireless communication with neighbors and kernel function with finite support, each sensor node independently learns its own Lagrange multipliers. During evaluation stage of learned regression function, each sensor node obtains a local result by communicating with local neighbors and estimates a global result by using a consensus algorithm. Simulations are provided to verify the proposed algorithm.