LAUSR

The hop-based positioning method is a straightforward, low-cost, and feasible positioning method. Most previous hop-based algorithms assume that the network is isotropic and uniformly distributed, which often does not reflect real-world conditions. In practice, the network may be anisotropic, which makes the hop count between nodes may not match the real distance well. As a result of this issue for hop-based positioning methods, in this paper, we propose a novel scheme that builds a skeleton model between anchor nodes to represent the anisotropy of a network. During the process of building the skeleton model, we use the corrected Akaike's Information Criterion (AICc), which can assist in the construction of a reliable and high accuracy skeleton model. With the help of the skeleton model with AICc, an unknown node can get a more accurate and reliable estimated position. The results of both theoretical analysis and experimental simulation show that the optimal hop-distance conversion model can be achieved, and compared to other similar algorithms, the proposed algorithm can obtain the position estimation result in a fast and accurate manner.