Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
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
Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions
SIAM Journal on Optimization
| Hi-index | 0.03 |
An adaptive rule-based algorithm, SpaseLoc, is described to solve localization problems for ad hoc wireless sensor networks. A large problem is solved as a sequence of very small subproblems, each of which is solved by semidefinite programming relaxation of geometric optimization model. The subproblems are generated according to a set of sensor/anchor selection rules and a priority list. Computational results compared with existing approaches show that the SpaseLoc algorithm scales well and provides excellent, positioning accuracy. A dynamic version of the SpaseLoc method is developed for estimating proving sensors locations in a real-time environment. The method uses dynamic distance measurement updates among sensors, and utilizes SpaseLoc for static sensor localization. Further computational results are presented, along with an application to bus transit systems.Ways to deploy sensor localization algorithms in clustered distributed environments are also studied, permitting application to arbitrarily large networks. In addition, we extend the algorithm to solving sensor localizations in 3D space. A preprocessor is developed to enable SpaseLoc: for localization of networks without absolute position information.* *Joint research conducted in the Dept of Management Science and Engineering, Stanford University.