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A Novel Distributed Clustering-based MDS Algorithm for Nodes Localization in WSNs
Guoyong Dai,Chunyu Miao,Yidong Li,Keji Mao,Qingzhang Chen 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.2
Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according to the tree structure formed by clustering. Simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.
Distributed Convex Optimization for Flocking of Nonlinear Multi-agent Systems
Qing Zhang,Zhikun Gong,Zhengquan Yang,Zengqiang Chen 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.5
A distributed optimization problem with differentiable convex objective function is discussed forcontinuous-time multi-agent systems with flocking behavior of a nonlinear continuous function. The goal of thispaper is to design a controller by using only local interaction information, thus making velocities of all agents be thesame. Then the stability of the multi-agent systems is proved and the velocities converge to the value minimizingthe sum of local objective functions. Moreover, the paper got some sufficient conditions for the consensus and theoptimization. Finally, a numerical case is used to verify the analytical results.