Abstract:
Inter-node distance measurements are crucial for network topology inference. Sparse distance measurements can lead to ambiguous realizations that differ greatly from the ground truth. Separators, 2D and 3D binary and triple vertex cut sets, cause flipping ambiguities.
This paper examines neighborhood, full graph, and component-level conditions for disambiguating flipping ambiguities caused by these separators. Thus, LFFC, GFFC, and CFFC are proposed. A combinatorial application of these conditions creates a disambiguating framework.
It detects separators and first disambiguates them locally by LFFC, which converts the graph to a binary tree with flipping-free leaf nodes and LFFC unsolvable edges. Then the CFFC condition disambiguates LFFC unsolvable separators between components.
LFFC and CFFC disambiguate k and g separators, reducing network localization ambiguous solutions by 2k+g times. Finally, flipping-free components realize node coordinates in their local coordinate systems, and a residue-based weighted component stitching algorithm (RWCS) is proposed to iteratively synchronize local coordinates to generate network global coordinates.
Extensive simulations show that the LFFC, CFFC, and RWCS frameworks are efficient, resolve most flipping ambiguities, and improve localization accuracy over state-of-the-art algorithms in sparse network settings.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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