Abstract:

After studying the topological structure of neighboring nodes in the WSN, we present a local localization (LLA) algorithm by combining the ideas of principal manifold learning and nonlinear dimension algorithm. This algorithm is particularly suitable for determining the relative locations of sensor nodes in the large-scale and low-density WSNs, where the low connectivity between nodes and the large ranging error between long-distance nodes usually make accurate localization quite difficult. In this algorithm, based on the pair-wise distance between each node and its neighbor nodes within a certain communication range, the local geometry of the global structure is firstly obtained by constructing a local subspace for each node, and those subspaces are then aligned to give the internal global coordinates of all nodes. Combined with the global structure and the anchor node information, we can finally calculate the absolute coordinates of all unknown nodes in the least squares algorithm.