Sensor network localization using kernel spectral regression.

This paper addresses the localization problem in wireless sensor networks using signal strength. We use a kernel function to measure the similarities between sensor nodes. The kernel matrix can be naturally defined in terms of the signal strength matrix. We show that the relative locations of sensor nodes can be obtained by solving a dimension reduction problem. To capture the structure of the whole network, we use the kernel spectral regression (KSR) method to estimate the relative locations of the sensor nodes. Given sufficient anchor nodes, the relative locations can be aligned to global locations. The key benefits of adopting KSR are that it allows us to define a graph to optimally preserve the topological structure of the sensor network, and a kernel function can capture the nonlinear relationship in the signal space. Simulation results show that we can achieve small average location error with a small number of anchors. We also compare our method with several related methods, and the results show that KSR is more efficient than the others in our simulated sensor networks. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]