Title :
Large-scale sensor network localization via rigid subnetwork registration
Author :
Chaudhury, Kunal N. ; Yuehaw Khoo ; Singer, Amit
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India
Abstract :
In this paper, we describe an algorithm for sensor network localization (SNL) that proceeds by dividing the whole network into smaller subnetworks, then localizes them in parallel using some fast and accurate algorithm, and finally registers the localized subnetworks in a global coordinate system. We demonstrate that this divide-and-conquer algorithm can be used to leverage existing high-precision SNL algorithms to large-scale networks, which could otherwise only be applied to small-to-medium sized networks. The main contribution of this paper concerns the final registration phase. In particular, we consider a least-squares formulation of the registration problem (both with and without anchor constraints) and demonstrate how this otherwise non-convex problem can be relaxed into a tractable convex program. We provide some preliminary simulation results for large-scale SNL demonstrating that the proposed registration algorithm (together with an accurate localization scheme) offers a good tradeoff between run time and accuracy.
Keywords :
concave programming; divide and conquer methods; least squares approximations; mathematical programming; signal processing; divide-and-conquer algorithm; global coordinate system; high-precision SNL algorithms; large-scale sensor network localization; least-squares formulation; nonconvex problem; rigid subnetwork registration; semidefinite programming; small-to-medium sized networks; tractable convex program; Accuracy; Noise; Noise measurement; Optimization; Programming; Standards; Transforms; anchors; divide-and-conquer; rigid registration; scalability; semidefinite programming; sensor network localization;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178491