A Graph Embedding Method for Wireless Sensor Networks Localization

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Wireless sensor location estimation is an important area and attracts considerable research interests. In this paper, the authors present a novel graph embedding method for the localization problem by using signal strengths. They view the wireless sensor nodes as a group of distributed devices, and employ an appropriate kernel function to measure the similarity between sensors. The kernel function can be naturally defined according to the signal strength matrix. Then they formulate the localization problem as a graph embedding problem. Finally, they use the Kernel Locality Preserving Projection (KLPP) technique to estimate the relative locations of all sensor nodes. Given sufficient number of anchors, the relative locations can be transformed into physical locations.