Optimization Letters

, Volume 8, Issue 2, pp 425–434

Routing-efficient CDS construction in Disk-Containment Graphs

  • Zaixin Lu
  • Lidong Wu
  • Panos M. Pardalos
  • Eugene Maslov
  • Wonjun Lee
  • Ding-Zhu Du
Original Paper

DOI: 10.1007/s11590-012-0590-5

Cite this article as:
Lu, Z., Wu, L., Pardalos, P.M. et al. Optim Lett (2014) 8: 425. doi:10.1007/s11590-012-0590-5

Abstract

In wireless networks, Connected Dominating Sets (CDSs) are widely used as virtual backbones for communications. On one hand, reducing the backbone size will reduce the maintenance overhead. So how to minimize the CDS size is a critical issue. On the other hand, when evaluating the performance of a wireless network, the hop distance between two communication nodes, which reflect the energy consumption and response delay, is of great importance. Hence how to minimize the routing cost is also a key problem for constructing the network virtual backbone. In this paper, we study the problem of constructing applicable CDS in wireless networks in terms of size and routing cost. We formulate a wireless network as a Disk-Containment Graph (DCG), which is a generalization of the Unit-Disk Graph (UDG), and we develop an efficient algorithm to construct CDS in such kind of graphs. The algorithm contains two parts and is flexible to balance the performance between the two metrics. We also analyze the algorithm theoretically. It is shown that our algorithm has provable performance in minimizing the CDS size and reducing the communication distance for routing.

Keywords

Wireless network CDS Routing efficiency Approximation algorithm 

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zaixin Lu
    • 1
  • Lidong Wu
    • 1
  • Panos M. Pardalos
    • 2
    • 3
  • Eugene Maslov
    • 3
  • Wonjun Lee
    • 4
  • Ding-Zhu Du
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA
  2. 2.Department of Industrial and System Engineering and Center for Applied OptimizationUniversity of FloridaGainesvilleUSA
  3. 3.Laboratory of Algorithms and Technologies for Network Analysis (LATNA), Higher School of EconomicsNational Research UniversityNizhny NovgorodRussia
  4. 4.Department of Computer Science and EngineeringKorea UniversityReoulRepublic of Korea

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