Edge Verifiability: Characterizing Outlier Measurements for Wireless Sensor Network Localization

A majority of localization approaches of wireless sensor networks rely on the measurements of inter-node distance. Errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, a straight-forward method, triangle inequality, is often employed in previous studies. However, triangle inequality has its own limitations that make itself far from accurate and reliable. In this study, we first analyze how much information are needed to identify outlier measurements. Applying rigidity theory, we propose the concept of verifiable edges and derive the conditions of an edge being verifiable. On this basis, we design a localization approach with outlier detection, which explicitly eliminates the rangings with large errors before location computation. Considering entire networks, we define verifiable graphs in which all edges are verifiable. If a wireless network meets the requirements of graph verifiability, it is not only localizable, but also outlier-resistant. Extensive simulations are conducted to examine the effectiveness of the proposed approach. The results show the remarkable improvements of location accuracy by sifting outliers.