Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms. Academic Article

abstract

• In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Wang, H., Liao, X., Wang, Z., Huang, T., & Chen, G.

citation count

• 9

complete list of authors

• Wang, Huiwei||Liao, Xiaofeng||Wang, Zidong||Huang, Tingwen||Chen, Guo

publication date

• January 2016

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Broadcast Gossip Algorithm
• Communication
• Computer Communication Networks
• Distributed Parameter Estimation
• Neural Networks, Computer
• Nonlinear Dynamics
• Quantized Communication
• Stochastic Processes
• Unreliable Sensor Networks

PubMed Central ID

• 26490219

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2015.09.008

start page

• 1

end page

• 9

volume

• 73

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neunet.2015.09.008