Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Sequence-Based Localization in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing
A soft computing approach to localization in wireless sensor networks
Expert Systems with Applications: An International Journal
Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
IEICE - Transactions on Information and Systems
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
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In this paper, we propose a new range-free localization algorithm called optimal proximity distance map using quadratic programming (OPDMQP). First, the relationship between geographical distances and proximity among sensor nodes in the given wireless sensor network is mathematically built. Then, the characteristics of the given network is represented as a set of constraints on the given network topology and the localization problem is formulated into a quadratic programming problem. Finally, the proposed method is applied to two anisotropic networks the topologies of which are very similar to those of the real-world applications. Unlike the most of previous localization methods which work well in the isotropic networks but not in the anisotropic networks, it is shown that the proposed method exhibits excellent and robust performances not only in the isotropic networks but also in the anisotropic networks.