Location information is essential for a wide range of wireless ad-hoc and sensor network applications. A number of localization approaches have been proposed, most of which are based on inter-node distance. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. In this thesis, we investigate a large body of existing approaches with focuses on error control, one rising aspect that attracts significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during cooperative localization process. We formally define the outlier detection problem for network localization and build a theoretical foundati...[ Read more ]

Location information is essential for a wide range of wireless ad-hoc and sensor network applications. A number of localization approaches have been proposed, most of which are based on inter-node distance. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. In this thesis, we investigate a large body of existing approaches with focuses on error control, one rising aspect that attracts significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during cooperative localization process. We formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design an outlier detection algorithm based on bilateration generic cycles, and examine its effectiveness and efficiency through a network prototype of MicaZ motes. Extensive simulations are also conducted on the data sets from a real-world system. The results show that our design significantly improves the localization accuracy by wisely rejecting outliers.