Global optimization and simulated annealing
Mathematical Programming: Series A and B
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Semidefinite programming for ad hoc wireless sensor network localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Simulated Annealing based Localization in Wireless Sensor Network
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Collaborative Localization in Wireless Sensor Networks
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Two-Phase Stochastic Optimization to Sensor Network Localization
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Localization based on stochastic optimization and RSSI measurements
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
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Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Self-organization and localization capabilities are one of the most important requirements in sensor networks. This paper provides an overview of centralized distance-based algorithms for estimating the positions of nodes in a sensor network. We discuss and compare three approaches: semidefinite programming, simulated annealing and two-phase stochastic optimization-a hybrid scheme that we have proposed. We analyze the properties of all listed methods and report the results of numerical tests. Particular attention is paid to our technique-the two-phase method-that uses a combination of trilateration, and stochastic optimization for performing sensor localization. We describe its performance in the case of centralized and distributed implementations.