Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Discrete & Computational Geometry
Theory of semidefinite programming for sensor network localization
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Connected rigidity matroids and unique realizations of graphs
Journal of Combinatorial Theory Series B
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Some theoretical aspects of position-location problems
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Graph drawing by stress majorization
GD'04 Proceedings of the 12th international conference on Graph Drawing
A survey on position-based routing in mobile ad hoc networks
IEEE Network: The Magazine of Global Internetworking
Technical Section: Mesh reconstruction by meshless denoising and parameterization
Computers and Graphics
Sensor network localization by eigenvector synchronization over the euclidean group
ACM Transactions on Sensor Networks (TOSN)
An efficient algorithm for mobile localization in sensor networks
International Journal of Automation and Computing
OFA: An optimistic approach to conquer flip ambiguity in network localization
Computer Networks: The International Journal of Computer and Telecommunications Networking
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We present a novel approach to localization of sensors in a network given a subset of noisy inter-sensor distances. The algorithm is based on “stitching” together local structures by solving an optimization problem requiring the structures to fit together in an “As-Rigid-As-Possible” manner, hence the name ARAP. The local structures consist of reference “patches” and reference triangles, both obtained from inter-sensor distances. We elaborate on the relationship between the ARAP algorithm and other state-of-the-art algorithms, and provide experimental results demonstrating that ARAP is significantly less sensitive to sparse connectivity and measurement noise. We also show how ARAP may be distributed.