IEICE TRANSACTIONS on Communications
Link Availability Based Mobility-Aware Max-Min Multi-Hop Clustering (M4C) for Mobile Ad Hoc Networks
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E92-B No.10 pp.3132-3142
- Publication Date
- 2009/10/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E92.B.3132
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Keyword
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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 -
English
