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Mathematical Problems in Engineering
Volume 2015, Article ID 165136, 7 pages
Research Article

A Topology Evolution Model Based on Revised PageRank Algorithm and Node Importance for Wireless Sensor Networks

1School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
2School of Computer Science and Technology, Xidian University, Xi’an 710071, China
3Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
4College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China

Received 25 November 2014; Revised 14 April 2015; Accepted 15 April 2015

Academic Editor: Elmetwally Elabbasy

Copyright © 2015 Xiaogang Qi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wireless sensor network (WSN) is a classical self-organizing communication network, and its topology evolution currently becomes one of the attractive issues in this research field. Accordingly, the problem is divided into two subproblems: one is to design a new preferential attachment method and the other is to analyze the dynamics of the network topology evolution. To solve the first subproblem, a revised PageRank algorithm, called Con-rank, is proposed to evaluate the node importance upon the existing node contraction, and then a novel preferential attachment is designed based on the node importance calculated by the proposed Con-rank algorithm. To solve the second one, we firstly analyze the network topology evolution dynamics in a theoretical way and then simulate the evolution process. Theoretical analysis proves that the network topology evolution of our model agrees with power-law distribution, and simulation results are well consistent with our conclusions obtained from the theoretical analysis and simultaneously show that our topology evolution model is superior to the classic BA model in the average path length and the clustering coefficient, and the network topology is more robust and can tolerate the random attacks.