Probabilistic region-based localization for wireless networks

Determining the physical location of wireless nodes is important to a wide variety of applications. In this paper, we propose a series of probabilistic region-based localization algorithms, including using static grids, segments of grids, and dynamic meshes. These algorithms provide a wide range of trade-off between accuracy and cost, making them suitable for different types of networks, such as sensor networks and mesh networks. Furthermore, we propose several techniques to extract and leverage additional information on location constraints, which is shown to significantly improve the localization accuracy and can be applied to other localization schemes. Finally we develop techniques to enhance robustness of localization, and show that the enhanced scheme can achieve high accuracy even in the presence of significant measurement errors.