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Wireless Networks

, Volume 22, Issue 4, pp 1235–1250 | Cite as

Social-aware dynamic router node placement in wireless mesh networks

  • Chun-Cheng Lin
  • Pei-Tsung Tseng
  • Ting-Yu Wu
  • Der-Jiunn DengEmail author
Article

Abstract

The problem of dynamic router node placement (dynRNP) in wireless mesh networks (WMNs) is concerned with determining a dynamic geographical placement of mesh routers to serve mobile mesh clients at different times, so that both network connectivity (i.e., the greatest topology subgraph component size) and client coverage (i.e., the number of the served mesh clients) are maximized. Mesh clients are wireless devises associated with users, and in real world, the users with same interests or some social relationship have higher chance to gather and move together geographically, i.e., they form a community, and the WMN with multiple communities can be regarded as a social network. Therefore, this paper investigates the so-called social-aware WMN-dynRNP problem assuming that mesh routers should be aware of the social community structure of mesh clients to dynamically adjust their placement to improve network performance. To cope with this problem, this paper proposes a social-based particle swarm optimization approach, which additionally includes a social-supporting vector to direct low-loading mesh routers to support the heavy-loading mesh routers in the same topology subgraph component (community), so as to dynamically adopt to the social community behavior of mesh clients. As compared with the previous approach, our experimental results show that the proposed approach is capable of effectively reducing number of the unserved mesh clients and increasing network connectivity in dynamic social scenarios.

Keywords

Social network Wireless mesh network Router node placement Community movement Particle swarm optimization 

Notes

Acknowledgments

The authors thank the anonymous referees for comments that improved the content as well as the presentation of this paper. This work has been supported in part by MOST 104-2221-E-009-134-MY2 and NSC 102-2221-E-018 -012 -MY3, Taiwan.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Chun-Cheng Lin
    • 1
  • Pei-Tsung Tseng
    • 1
  • Ting-Yu Wu
    • 1
  • Der-Jiunn Deng
    • 2
    Email author
  1. 1.Department of Industrial Engineering and ManagementNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Changhua University of EducationChanghuaTaiwan

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