Approximating Size of Maximal Clique in a Node's Neighbourhood in the Ad Hoc Networks

Borovina, Nihad; Kreso, Sead

doi:10.1109/wcnc.2009.4917601

Cited by 1 publication

(4 citation statements)

References 10 publications

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“…With this approach we avoid random input distribution, which can be a source of an unnecessary long time for algorithm processing. In conjunction with such ordering, we will try to reduce the number of edges using the metric presented in [13].…”

Section: Proposed Approachmentioning

confidence: 99%

“…Reduction is done by using the algorithm presented in [13] which has as a goal to approximate the size of the maximum clique in the neighborhood of the specific node (in the following we will label it as Approximation algorithm).…”

Section: Reduction Of the Number Of The Nodes (Edges) In Ptramentioning

confidence: 99%

“…4) or set Nu\{S 1 US 2 }. This is a fast algorithm running in O(¨5) time [13], where ¨ is the maximum node degree in the node's neighborhood.…”

Section: Reduction Of the Number Of The Nodes (Edges) In Ptramentioning

confidence: 99%

“…Originally, the Approximation algorithm in [13] starts with cardinality of N u as an input. If the resulting sets S 1 and S 2 are empty, the input value is reduced by one, and the algorithm is executed again.…”

Section: Main Proceduresmentioning

confidence: 99%

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New edge sorting criterion for maximum clique search algorithm in unit disk graphs

Peruničić

Borovina

2011

2011 XXIII International Symposium on Information, Communication and Automation Technologies

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Unit disk graphs (UDG) are a natural choice for wireless network modeling. However, complex UDG algorithms with a long processing time could be ineffective because of nodes mobility and the lack of processing power of the nodes. Therefore, they should be adapted for use in wireless networks. In this paper we present an accelerated algorithm for maximum clique in UDG using simple calculations. The input of the algorithm is an ordered set of graph edges. Our approach is to decrease the number of edges involved in algorithm, and then to reorder the edges for faster processing. The ordering criterion uses two-hop neighborhood information. The simulation results show a significant shortening of algorithm processing time. Two types of node distribution, x-y and ȡ-ĳ were tested. The results depend on the distribution type. This indicates a possibility to create adaptive algorithms fitted to a specific type of node distribution in the network.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Reduction Of the Number Of The Nodes (Edges) In Ptramentioning

confidence: 99%

“…4) or set Nu\{S 1 US 2 }. This is a fast algorithm running in O(¨5) time [13], where ¨ is the maximum node degree in the node's neighborhood.…”

Section: Reduction Of the Number Of The Nodes (Edges) In Ptramentioning

confidence: 99%

Section: Main Proceduresmentioning

confidence: 99%

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