The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Redes Ad Hoc Móveis (MANETs) possuem topologias inerentemente dinâmicas. Devido à natureza distribuída e multi-hop destas redes, a mobilidade aleatória dos nós não só afecta a disponibilidade de ligações de rádio entre pares de nós específicos, mas também ameaça a fiabilidade dos caminhos de comunicação, a descoberta de serviços e até mesmo a qualidade do serviço das MANETs. Neste artigo, um novo modelo de cadeia de Markov é apresentado para prever a disponibilidade de links para MANETs. Com base em uma estimativa aproximada da distância inicial entre dois nós, a abordagem proposta é capaz de estimar com precisão a disponibilidade do enlace em um ambiente de mobilidade aleatória. Além disso, a abordagem proposta para estimativa de disponibilidade de enlace é integrada à heurística de d-clustering Max-Min. A heurística de clustering aprimorada, chamada M4C, leva em consideração a mobilidade dos nós ao agrupar nós móveis em clusters. Os resultados da simulação são fornecidos para verificar a abordagem e a melhoria do desempenho do algoritmo de clustering. Também demonstra a adaptabilidade M4C, e mostra que M4C é capaz de alcançar um equilíbrio entre a eficácia da agregação de topologia e as estabilidades do cluster. O algoritmo proposto também pode ser utilizado para melhorar a disponibilidade e qualidade dos serviços para MANETs.
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Yuebin BAI, Jun HUANG, Qingmian HAN, Depei QIAN, "Link Availability Based Mobility-Aware Max-Min Multi-Hop Clustering (M4C) for Mobile Ad Hoc Networks" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 10, pp. 3132-3142, October 2009, doi: 10.1587/transcom.E92.B.3132.
Abstract: Mobile Ad Hoc Networks (MANETs) have inherently dynamic topologies. Due to the distributed, multi-hop nature of these networks, random mobility of nodes not only affects the availability of radio links between particular node pairs, but also threatens the reliability of communication paths, service discovery, even quality of service of MANETs. In this paper, a novel Markov chain model is presented to predict link availability for MANETs. Based on a rough estimation of the initial distance between two nodes, the proposed approach is able to accurately estimate link availability in a random mobility environment. Furthermore, the proposed link availability estimation approach is integrated into Max-Min d-clustering heuristic. The enhanced clustering heuristic, called M4C, takes node mobility into account when it groups mobile nodes into clusters. Simulation results are given to verify the approach and the performance improvement of clustering algorithm. It also demonstrates the adaptability of M4C, and shows that M4C is able to achieve a tradeoff between the effectiveness of topology aggregation and cluster stabilities. The proposed algorithm can also be used to improve the availability and quality of services for MANETs.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.3132/_p
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@ARTICLE{e92-b_10_3132,
author={Yuebin BAI, Jun HUANG, Qingmian HAN, Depei QIAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Link Availability Based Mobility-Aware Max-Min Multi-Hop Clustering (M4C) for Mobile Ad Hoc Networks},
year={2009},
volume={E92-B},
number={10},
pages={3132-3142},
abstract={Mobile Ad Hoc Networks (MANETs) have inherently dynamic topologies. Due to the distributed, multi-hop nature of these networks, random mobility of nodes not only affects the availability of radio links between particular node pairs, but also threatens the reliability of communication paths, service discovery, even quality of service of MANETs. In this paper, a novel Markov chain model is presented to predict link availability for MANETs. Based on a rough estimation of the initial distance between two nodes, the proposed approach is able to accurately estimate link availability in a random mobility environment. Furthermore, the proposed link availability estimation approach is integrated into Max-Min d-clustering heuristic. The enhanced clustering heuristic, called M4C, takes node mobility into account when it groups mobile nodes into clusters. Simulation results are given to verify the approach and the performance improvement of clustering algorithm. It also demonstrates the adaptability of M4C, and shows that M4C is able to achieve a tradeoff between the effectiveness of topology aggregation and cluster stabilities. The proposed algorithm can also be used to improve the availability and quality of services for MANETs.},
keywords={},
doi={10.1587/transcom.E92.B.3132},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Link Availability Based Mobility-Aware Max-Min Multi-Hop Clustering (M4C) for Mobile Ad Hoc Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3132
EP - 3142
AU - Yuebin BAI
AU - Jun HUANG
AU - Qingmian HAN
AU - Depei QIAN
PY - 2009
DO - 10.1587/transcom.E92.B.3132
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2009
AB - Mobile Ad Hoc Networks (MANETs) have inherently dynamic topologies. Due to the distributed, multi-hop nature of these networks, random mobility of nodes not only affects the availability of radio links between particular node pairs, but also threatens the reliability of communication paths, service discovery, even quality of service of MANETs. In this paper, a novel Markov chain model is presented to predict link availability for MANETs. Based on a rough estimation of the initial distance between two nodes, the proposed approach is able to accurately estimate link availability in a random mobility environment. Furthermore, the proposed link availability estimation approach is integrated into Max-Min d-clustering heuristic. The enhanced clustering heuristic, called M4C, takes node mobility into account when it groups mobile nodes into clusters. Simulation results are given to verify the approach and the performance improvement of clustering algorithm. It also demonstrates the adaptability of M4C, and shows that M4C is able to achieve a tradeoff between the effectiveness of topology aggregation and cluster stabilities. The proposed algorithm can also be used to improve the availability and quality of services for MANETs.
ER -