%0 Conference Paper
%B IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS)
%D 2011
%T An Effective Metaheuristic Approach to Node Localization in Wireless Sensor Networks
%A Massimo Vecchio
%A R. López-Valcarce
%A Francesco Marcelloni
%B IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS)
%G eng
%0 Conference Paper
%B the 3-th IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)
%D 2011
%T Solving the Node Localization Problem in WSNs by a Two-objective Evolutionary Algorithm and Gradient Descent
%A Massimo Vecchio
%A R. López-Valcarce
%A Francesco Marcelloni
%B the 3-th IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)
%G eng
%0 Conference Paper
%B IEEE International Conference on Intelligent Systems Design and Applications (ISDA)
%D 2011
%T A study on the Application of Different Two-objective Evolutionary Algorithms to the Node Localization Problem in Wireless Sensor Networks
%A Massimo Vecchio
%A R. López-Valcarce
%A Francesco Marcelloni
%B IEEE International Conference on Intelligent Systems Design and Applications (ISDA)
%G eng
%0 Journal Article
%J Applied Soft Computing
%D 2011
%T A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks
%A Massimo Vecchio
%A R. López-Valcarce
%A Francesco Marcelloni
%K dynacs
%K Multiobjective evolutionary algorithms
%K Node localization
%K Range measurements
%K Stochastic optimization
%K wsn
%X To know the location of nodes plays an important role in many current and envisioned wireless sensor network applications. In this framework, we consider the problem of estimating the locations of all the nodes of a network, based on noisy distance measurements for those pairs of nodes in range of each other, and on a small fraction of anchor nodes whose actual positions are known a priori. The methods proposed so far in the literature for tackling this non-convex problem do not generally provide accurate estimates. The difficulty of the localization task is exacerbated by the fact that the network is not generally uniquely localizable when its connectivity is not sufficiently high. In order to alleviate this drawback, we propose a two-objective evolutionary algorithm which takes concurrently into account during the evolutionary process both the localization accuracy and certain topological constraints induced by connectivity considerations. The proposed method is tested with different network configurations and sensor setups, and compared in terms of normalized localization error with another metaheuristic approach, namely SAL, based on simulated annealing. The results show that, in all the experiments, our approach achieves considerable accuracies and significantly outperforms SAL, thus manifesting its effectiveness and stability.

%B Applied Soft Computing
%V 12
%P 1891-1901
%8 07/2012
%G eng
%N 7
%R 10.1016/j.asoc.2011.03.012