Sum of squares method for sensor network localization
Computational Optimization and Applications
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
Successive and asymptotically efficient localization of sensor nodes in closed-form
IEEE Transactions on Signal Processing
Distributed wireless sensor network localization using stochastic proximity embedding
Computer Communications
A graph embedding method for wireless sensor networks localization
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Sequential greedy localization in wireless sensor networks with inaccurate anchor positions
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Distributed wireless sensor network localization via sequential greedy optimization algorithm
IEEE Transactions on Signal Processing
On the optimal performance of collaborative position location
IEEE Transactions on Wireless Communications
Universal rigidity: towards accurate and efficient localization of wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions
SIAM Journal on Optimization
Universal Rigidity and Edge Sparsification for Sensor Network Localization
SIAM Journal on Optimization
A trust region SQP-filter method for nonlinear second-order cone programming
Computers & Mathematics with Applications
ACM Transactions on Mathematical Software (TOMS)
Accurate sequential self-localization of sensor nodes in closed-form
Signal Processing
Computational Optimization and Applications
A minimax probabilistic approach to feature transformation for multi-class data
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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The sensor network localization problem has been much studied. Recently Biswas and Ye proposed a semidefinite programming (SDP) relaxation of this problem which has various nice properties and for which a number of solution methods have been proposed. Here, we study a second-order cone programming (SOCP) relaxation of this problem, motivated by its simpler structure and its potential to be solved faster than SDP. We show that the SOCP relaxation, though weaker than the SDP relaxation, has nice properties that make it useful as a problem preprocessor. In particular, sensors that are uniquely positioned among interior solutions of the SOCP relaxation are accurate up to the square root of the distance error. Thus, these sensors, which are easily identified, are accurately positioned. In our numerical simulation, the interior solution found can accurately position up to 80-90&percent; of the sensors. We also propose a smoothing coordinate gradient descent method for finding an interior solution that is faster than an interior-point method.