Distributed wireless sensor network localization using stochastic proximity embedding

In this paper, we propose a computationally efficient distributed Wireless Sensor Network (WSN) localization method based on Stochastic Proximity Embedding (SPE), which is a dimensionality reduction technique that finds a low dimensional embedding of a high dimensional data by preserving the pair-wise distance data information. Unlike the localization techniques based on classical Multidimensional Scaling (MDS), which is a popular dimensionality reduction technique, SPE method does not require a complete distance information matrix of the network and it scales linearly with the number of nodes in the network. Also the stochastic descent approach adopted in SPE provides an accurate position estimate in reasonable number of iterations. Through extensive simulation study of the proposed method, it is found to provide better results in both uniform and irregular shaped sensor networks.