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Efficient combination policies for diffusion adaptive networks

  • Jie Wang
  • Fei Dai
  • Jie Yang
  • Guan GuiEmail author
Article
  • 2 Downloads

Abstract

Diffusion adaptive networks (DANs) have many applications such as signal processing, mobile wireless sensor network and the internet of things (IoT). Unlike the classical centralized networks, a DAN uses the information exchange among local neighbors to solve global problems. The performance of the DAN highly depends on the combination matrix policies, which raises the issue of the optimal selection of the combination matrix. However, traditional combination policies focus on either the steady-state error or the convergence speed. Inspired by the solution of minimizing the mean square deviation (MSD) of the DAN, this paper proposes two efficient adaptive combination policies: 1) relative-instantaneous-error combination policy and 2) relative-deviation combination policy. These two policies are related to the inverse of noise by different metrics. Computer simulations verify that the proposed combination policies outperform the existing combination rules in either steady-state error or convergence rate in various noise environments. Finally, we apply the two combined rules to the collaborative target-tracking problem and achieve expected results.

Keywords

Combination policy Collaborative target tracking Diffusion adaptive network Signal processing Internet of things (IoT) 

Notes

Acknowledgements

This work was funded by the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China Grants (No. 61701258, No. 61501223), Jiangsu Specially Appointed Professor Program (No. RK002STP16001), Summit of the Six Top Talents Program of Jiangsu (No. XYDXX-010), Program for High-Level Entrepreneurial and Innovative Talents Introduction (No. CZ0010617002), NJUPTSF (No. NY215026) and 1311 Talent Plan of NJUPT.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information and Telecommunication EngineeringNanjing University of Posts and TelecommunicationsNanjingChina

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