Path loss exponent estimation for wireless sensor network localization
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Signal Processing
Optimization Schemes For Wireless Sensor Network Localization
International Journal of Applied Mathematics and Computer Science
Bio-inspired node localization in wireless sensor networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Localization research based on improved simulated annealing algorithm in WSN
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Graphical properties of easily localizable sensor networks
Wireless Networks
Distributed wireless sensor network localization using stochastic proximity embedding
Computer Communications
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
VLOCI2: improving 2D location coordinates using distance measurements in GPS-equipped VANETs
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Finding lower bounds of localization with noisy measurements using genetic algorithms
Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
Engineering Applications of Artificial Intelligence
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In sensor networks, the information obtained from sensors will be meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. Simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems.The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes.