Routingefficient CDS construction in DiskContainment Graphs
 Zaixin Lu,
 Lidong Wu,
 Panos M. Pardalos,
 Eugene Maslov,
 Wonjun Lee,
 DingZhu Du
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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 DiskContainment Graph (DCG), which is a generalization of the UnitDisk 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.
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 Introduction
 Preliminaries
 Related works
 Algorithm
 Theoretical analysis
 Conclusion
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 Title
 Routingefficient CDS construction in DiskContainment Graphs
 Journal

Optimization Letters
Volume 8, Issue 2 , pp 425434
 Cover Date
 20140201
 DOI
 10.1007/s1159001205905
 Print ISSN
 18624472
 Online ISSN
 18624480
 Publisher
 Springer Berlin Heidelberg
 Additional Links
 Topics
 Keywords

 Wireless network
 CDS
 Routing efficiency
 Approximation algorithm
 Industry Sectors
 Authors

 Zaixin Lu ^{(1)}
 Lidong Wu ^{(1)}
 Panos M. Pardalos ^{(2)} ^{(3)}
 Eugene Maslov ^{(3)}
 Wonjun Lee ^{(4)}
 DingZhu Du ^{(1)}
 Author Affiliations

 1. Department of Computer Science, University of Texas at Dallas, Richardson, TX, 75080, USA
 2. Department of Industrial and System Engineering and Center for Applied Optimization, University of Florida, Gainesville, FL, 32611, USA
 3. Laboratory of Algorithms and Technologies for Network Analysis (LATNA), Higher School of Economics, National Research University, 136 Rodionova St., Nizhny Novgorod, 603093, Russia
 4. Department of Computer Science and Engineering, Korea University, Reoul, Republic of Korea