DCMC - delay-constrained multipoint communication with multiple sources

Karaman, A.; Hassanein, H.

doi:10.1109/iscc.2003.1214162

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…In order to maximize sav, we have to apply the Minimum Spanning Tree (MST) algorithm to each tree in Eq. (10). However, when the number of trees is large, the required number of applications of the MST algorithm in each step of Eq.…”

Section: Node Exchange Algorithm (Nea)mentioning

confidence: 99%

“…The DCMST problem finds the least cost broadcast and multicast trees rooted at the source node which have the minimum total cost among all possible spanning trees having a maximum end-to-end delay less than or equal to a given delay constraint D [10,16,17,18,22,23]. The DCMST problem is used for real-time multimedia applications that have rigid end-to-end delay requirements and consume large amount of network resource.…”

Section: Introductionmentioning

confidence: 99%

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Least cost heuristic for the delay-constrained capacitated minimum spanning tree problem

Lee

Atiquzzaman

2005

Computer Communications

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Section: Node Exchange Algorithm (Nea)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Least cost heuristic for the delay-constrained capacitated minimum spanning tree problem

Lee

Atiquzzaman

2005

Computer Communications

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“…In the literature, the works related to topology discovery of local networks can be classified in two main problems: Capacitated Minimum Spanning Tree (CMST) and Delay-Constrained Minimum Spanning Tree (DCMST) problems [6][7][8][9][10]. The CMST problem consists of finding a set of minimum spanning trees rooted at the source node which satisfies a set of traffic constraints [8].…”

Section: Introductionmentioning

confidence: 99%

“…This problem is known to be NP-complete [9], and several heuristic approaches have been applied to solve it [6,7]. On the other hand, the DCMST problem consists of finding the least cost broadcast and multi-cast trees rooted at the source node, which have the minimum total cost among all possible spanning trees, and also have a maximum end-to-end delay bounded by a given delay constraint D [10,11].…”

Section: Introductionmentioning

confidence: 99%

A dandelion-encoded evolutionary algorithm for the delay-constrained capacitated minimum spanning tree problem

Pérez-Bellido

Salcedo‐Sanz

Ortiz-García

et al. 2009

Computer Communications

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a b s t r a c tThis paper proposes an evolutionary algorithm with Dandelion-encoding to tackle the Delay-Constrained Capacitated Minimum Spanning Tree (DC-CMST) problem. This problem has been recently proposed, and consists of finding several broadcast trees from a source node, jointly considering traffic and delay constraints in trees. A version of the problem in which the source node is also included in the optimization process is considered as well in the paper. The Dandelion code used in the proposed evolutionary algorithm has been recently proposed as an effective way of encoding trees in evolutionary algorithms. Good properties of locality has been reported on this encoding, which makes it very effective to solve problems in which the solutions can be expressed in form of trees. In the paper we describe the main characteristics of the algorithm, the implementation of the Dandelion-encoding to tackled the DC-CMST problem and a modification needed to include the source node in the optimization. In the experimental section of this article we compare the results obtained by our evolutionary with that of a recently proposed heuristic for the DC-CMST, the Least Cost (LC) algorithm. We show that our Dandelion-encoded evolutionary algorithm is able to obtain better results that the LC in all the instances tackled.

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“…Many centralized multi-core selection algorithms have been proposed for wired networks [1], [6], [7]. These solutions are greedy variations of either the d-hop DS problem, or the kcenter problem.…”

Section: Related Workmentioning

confidence: 99%

Multicasting in ad hoc networks in the context of multiple channels and multiple interfaces

Spohn

Garcia-Luna-Aceves

IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005.

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Abstract-Multicast routing protocols based on shared trees employ one or more rendezvous points (usually called cores) for coordination. To address fault tolerance in case of core failure, multiple cores can be deployed. The location of cores is crucial for the performance of the protocol. In this context, the problem of finding the location for the cores is similar to the (k, r)-dominating set problem, (k, r)-DS, in graph theory. That is, (k, r)-DS is defined as the problem of selecting a subset of nodes D such that the remaining nodes are within distance r from at least k nodes in D. In mobile ad hoc networks (MANETs), finding the location of cores should be computed distributively, because the topology may change frequently. We present a distributed solution to the (k, r)-DS problem, named DKR, which is used for core selection in a novel multicast protocol named core hierarchical election for multicasting in ad hoc networks (CHEMA). CHEMA is designed to operate in the context of multiple channels and multiple interfaces. One interface is dedicated for the communication among cores and members, using a non-interfering channel. The performance of CHEMA is compared against one of the best performing multicast protocols to date. CHEMA is shown to perform better in all scenarios considered.

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DCMC - delay-constrained multipoint communication with multiple sources

Cited by 11 publications

References 4 publications

Least cost heuristic for the delay-constrained capacitated minimum spanning tree problem

Least cost heuristic for the delay-constrained capacitated minimum spanning tree problem

A dandelion-encoded evolutionary algorithm for the delay-constrained capacitated minimum spanning tree problem

Multicasting in ad hoc networks in the context of multiple channels and multiple interfaces

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