A Range-Free Node Localization Method for Anisotropic Wireless Sensor Networks with Sparse Anchors

TL;DR

This paper presents AW-MinMax, an adaptive weighted method for range-free node localization in anisotropic wireless sensor networks with sparse anchors, improving accuracy and stability through convex optimization and iterative refinement.

Contribution

Introduces AW-MinMax, a novel adaptive weighted localization method that handles non-convex constraints via matrix transformations and sequential convex approximation.

Findings

Significantly improves localization accuracy in irregular sensor networks.

Enhances stability of node position estimates.

Outperforms existing methods in simulation tests.

Abstract

In sensor networks characterized by irregular layouts and poor connectivity, anisotropic properties can significantly reduce the accuracy of distance estimation between nodes, consequently impairing the localization precision of unidentified nodes. Since distance estimation is contingent upon the multi-hop paths between anchor node pairs, assigning differential weights based on the reliability of these paths could enhance localization accuracy. To address this, we introduce an adaptive weighted method, termed AW-MinMax, for range-free node localization. This method involves constructing a weighted mean nodes localization model, where each multi-hop path weight is inversely proportional to the number of hops. Despite the model's inherent non-convexity and non-differentiability, it can be reformulated into an optimization model with convex objective functions and non-convex constraints through matrix transformations. To resolve these constraints, we employ a Sequential Convex Approximation (SCA) algorithm that utilizes first-order Taylor expansion for iterative refinement. Simulation results validate that our proposed algorithm substantially improves stability and accuracy in estimating range-free node locations.