Robust graph-based localization for industrial Internet of things in the presence of flipping ambiguities

Localisation of machines in harsh Industrial Internet of Things (IIoT) environment is necessary for various applications. Therefore, a novel localisation algorithm is proposed for noisy range measurements in IIoT networks. The position of an unknown machine device in the network is estimated using the relative distances between blind machines (BMs) and anchor machines (AMs). Moreover, a more practical and challenging scenario with the erroneous position of AM is considered, which brings additional uncertainty to the final position estimation. Therefore, the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced. Only those AMs will participate in the localisation process, which increases the accuracy of the final location estimate. Then, the closed-form expression of the proposed greedy successive anchorization process is derived, which prevents possible local convergence, reduces computation, and achieves Cramér-Rao lower bound accuracy for white Gaussian measurement noise. The results are compared with the state-of-the-art and verified through numerous simulations.