Algorithm 875: DSDP5—software for semidefinite programming
ACM Transactions on Mathematical Software (TOMS)
A note on the trackability of dynamic sensor networks
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Sensor network localization, euclidean distance matrix completions, and graph realization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
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International Journal of Systems Science - Networked Control Systems and Wireless Sensor Networks - Theories and Applications
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Computational Optimization and Applications
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IEEE Transactions on Signal Processing
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ACM Transactions on Sensor Networks (TOSN)
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ACM Transactions on Sensor Networks (TOSN)
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ACM Transactions on Sensor Networks (TOSN)
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SIAM Journal on Optimization
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Mathematics of Operations Research
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SIAM Journal on Optimization
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Communications of the ACM
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The Journal of Machine Learning Research
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Computational Optimization and Applications
ACM Transactions on Mathematical Software (TOMS)
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RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
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Discrete Applied Mathematics
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We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in $$\mathcal{R}^2$$ using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.