Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Simple metaheuristics using the simplex algorithm for non-linear programming
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Semi-Supervised Learning
Quantized incremental algorithms for distributed optimization
IEEE Journal on Selected Areas in Communications
Two-fold spatiotemporal regression modeling in wireless sensor networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
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Wireless sensor networks (WSNs) have been of great interest among academia and industry, due to their diverse applications in recent years. The main goal of a WSN is data collection. As the amount of the collected data increases, it would be essential to develop some techniques to analyze them. In this paper, we propose an in-network optimization algorithm based on Nelder-Mead simplex (NM simplex) to incrementally do regression analysis over distributed data. Then improve the regression accuracy by the use of re-sampling in each node. Simulation results show that the proposed algorithm not only increases the accuracy to more than that of the centralized approach, but is also more efficient in terms of communication compared to its counterparts.