Optimal Measurement Geometry Directed Integrated Localization and Synchronization in Large-Scale Wireless Networks

Location awareness and time consensus, which are two intertwined aspects of distributed systems, have become more important in vertical industrial Internet of Things (IoT) applications. Existing integrated localization and synchronization (ILAS) in a connected system relies on collaborative measurement of time of arrival, as well as exchange of estimated location and clock related states. However, with the growing scale and dynamics of wireless IoT systems, the unselected and excessive information obtained from the collaborating nodes becomes less effective in ILAS. To enhance the performance of ILAS with controlled complexity, we first propose an optimal measurement geometry directed collaborating nodes selection scheme in this paper. Specifically, the optimal measurement geometry evaluated by the dilution of precision is utilized to prioritize the corresponding subset collaborating nodes for the best estimation accuracy with limited complexity. Moreover, to further reduce the computation complexity in increased-scale systems, a sequential state stacking belief propagation algorithm is proposed for the related states estimation, where the matrix inversions and square root calculations reduce to the dimensions of a subset of the overall collaborating states. Numerical simulations demonstrate a significant enhancement in the robustness of the ILAS estimation and reduction in the computational complexity compared to the baseline schemes.