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Open AccessJournal ArticleDOI

Approximation Algorithms for Connected Dominating Sets

TLDR
In this paper, a polynomial time algorithm with a (c+1) H(Δ) +c-1 approximation factor was proposed, where c is the Steiner approximation ratio.
Abstract
The dominating set problem in graphs asks for a minimum size subset of vertices with the following property: each vertex is required to be either in the dominating set, or adjacent to some vertex in the dominating set. We focus on the related question of finding a connected dominating set of minimum size, where the graph induced by vertices in the dominating set is required to be connected as well. This problem arises in network testing, as well as in wireless communication. Two polynomial time algorithms that achieve approximation factors of 2H(Δ)+2 and H(Δ)+2 are presented, where Δ is the maximum degree and H is the harmonic function. This question also arises in relation to the traveling tourist problem, where one is looking for the shortest tour such that each vertex is either visited or has at least one of its neighbors visited. We also consider a generalization of the problem to the weighted case, and give an algorithm with an approximation factor of (cn+1) \ln n where cn ln k is the approximation factor for the node weighted Steiner tree problem (currently cn = 1.6103 ). We also consider the more general problem of finding a connected dominating set of a specified subset of vertices and provide a polynomial time algorithm with a (c+1) H(Δ) +c-1 approximation factor, where c is the Steiner approximation ratio for graphs (currently c = 1.644 ).

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Citations
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Proceedings ArticleDOI

On calculating connected dominating set for efficient routing in ad hoc wireless networks

TL;DR: In this paper, the authors proposed a simple and efficient distributed algorithm for calculating connected dominating set in ad-hoc wireless networks, where connections of nodes are determined by their geographical distances.
Journal ArticleDOI

Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks

TL;DR: This paper proposes to significantly reduce or eliminate the communication overhead of a broadcasting task by applying the concept of localized dominating sets, which do not require any communication overhead in addition to maintaining positions of neighboring nodes.
Proceedings ArticleDOI

A clustering scheme for hierarchical control in multi-hop wireless networks

TL;DR: This paper presents a clustering scheme to create a hierarchical control structure for multi-hop wireless networks and presents an efficient distributed implementation of the clustering algorithm for a set of wireless nodes to create the set of desired clusters.
Journal ArticleDOI

An extended localized algorithm for connected dominating set formation in ad hoc wireless networks

TL;DR: This paper proposes a dominant pruning rule (called Rule k) that is more effective in reducing the dominating set derived from the marking process than the combination of Rules 1 and 2 and, surprisingly, in a restricted implementation with local neighborhood information, Rule k has the same communication complexity and less computation complexity.
Proceedings ArticleDOI

Multicast tree construction and flooding in wireless ad hoc networks

TL;DR: After it is pointed out that constructing minimum cost multicast tree is hard, two new flooding methods are proposed, self pruning and dominant pruning, which utilize neighbor information to reduce redundant transmissions.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI

A threshold of ln n for approximating set cover

TL;DR: It is proved that (1 - o(1) ln n setcover is a threshold below which setcover cannot be approximated efficiently, unless NP has slightlysuperpolynomial time algorithms.
Journal ArticleDOI

On the hardness of approximating minimization problems

TL;DR: It is proved that there is an e > 0 such that Graph Coloring cannot be approximated with ratio n e unless P = NP, and Set Covering cannot be approximation with ratio c log n for any c < 1/4 unless NP is contained in DTIME(n poly log n).
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