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SMM: mathematical framework of a scalable mobility model

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J. IrvineAuthors Info & Claims

MSWIM '03: Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems

Pages 74 - 81

https://doi.org/10.1145/940991.941005

Published: 19 September 2003 Publication History

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Abstract

In this paper, we present a novel mathematical framework of a mobility model that can be applied to a large number of possible horizontal environments, ranging from local area networks (LANs) to wide area networks (WANs) for the prediction and tracking of mobile users. This new mobility model, termed 'Scalable Mobility Model' (SMM), provides a realistic set of paths for both individual and aggregate subscriber movement by assigning mobile users into specific classes of mobility based on their mobility characteristics, attraction points, geographical environments and time periods. The core technique used to implement these important mobility features in SMM is the introduction of a new concept referred to as the Pole of Gravity. Our mobility model has been decomposed into three processes termed as the physical, gravity and fluid sub-models. Using this new concept, we show how SMM can effectively characterize user mobility for the City Area Model of Avon district and the City Center of Bristol, UK, having an extension of 40 km by 40 km and 8 km by 8 km respectively. We also present simulation results to illustrate the effect of accurate mobility by comparing our realistic mobility model, SMM, with the well know Random Waypoint model. Specifically, we show how the choice of a mobility model affects channel utilization and handover performance issues for the mobile environment.

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Cited By

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Gu TZhu MChen WHuang ZMaciejewski RChang L(2018)Structuring Mobility Transition With an Adaptive Graph RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28584395:4(1121-1132)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2858439
Hess AHummel KGansterer WHaring G(2015)Data-driven Human Mobility ModelingACM Computing Surveys10.1145/284072248:3(1-39)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2840722
Treurniet J(2014)A Taxonomy and Survey of Microscopic Mobility Models from the Mobile Networking DomainACM Computing Surveys10.1145/261697347:1(1-32)Online publication date: 14-Jul-2014
https://dl.acm.org/doi/10.1145/2616973
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Index Terms

SMM: mathematical framework of a scalable mobility model
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

MSWIM '03: Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems

September 2003

152 pages

ISBN:1581137664

DOI:10.1145/940991

General Chair:
Rassul Ayani
Royal Institute of Technology, Sweden
,
Program Chairs:
Carla-Fabiana Chiasserini
Politecnico di Torino, Italy
,
Hossam Hassanein
Queen's University, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 September 2003

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MSWiM03

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SIGMOBILE

MSWiM03: 6th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems ( Co-located with MOBICOM 2003 Conference )

September 19, 2003

CA, San Diego, USA

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MSWIM '03 Paper Acceptance Rate 21 of 100 submissions, 21%;

Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

View all

Gu TZhu MChen WHuang ZMaciejewski RChang L(2018)Structuring Mobility Transition With an Adaptive Graph RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28584395:4(1121-1132)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2858439
Hess AHummel KGansterer WHaring G(2015)Data-driven Human Mobility ModelingACM Computing Surveys10.1145/284072248:3(1-39)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2840722
Treurniet J(2014)A Taxonomy and Survey of Microscopic Mobility Models from the Mobile Networking DomainACM Computing Surveys10.1145/261697347:1(1-32)Online publication date: 14-Jul-2014
https://dl.acm.org/doi/10.1145/2616973
Roy RRoy R(2010)Gravity MobilityHandbook of Mobile Ad Hoc Networks for Mobility Models10.1007/978-1-4419-6050-4_14(443-482)Online publication date: 30-Aug-2010
https://doi.org/10.1007/978-1-4419-6050-4_14
Park MRodoplu V(2009)Energy Maps for Mobile Wireless NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2009.328:6(778-791)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1109/TMC.2009.32
Murugappan KSelvi V(2006)Veridical Mobility Model for Ad Hoc Network2006 International Symposium on Ad Hoc and Ubiquitous Computing10.1109/ISAHUC.2006.4290662(142-147)Online publication date: Dec-2006
https://doi.org/10.1109/ISAHUC.2006.4290662

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