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Experimental validation of the random waypoint mobility model through a real world mobility trace for large geographical areas

Published: 10 October 2005 Publication History

Abstract

User mobility models are used in simulations of mobile communications systems to study characteristics of network performance. One of the models which is in common use is the Random Waypoint Model (RWP). The RWP is a simple mobility model based on random destinations, speeds and pause times. The RWP is often criticised as not representing how humans actually move. Paradoxically, validation against real mobility data is seen as being difficult due to the impracticalities of obtaining real mobility data.We give details of a real world user movement trace from which we obtained data about one individual's destinations, travel routes, average speed and rest times whilst moving throughout a city-wide area. We present results from this real life data and use it to validate some of the key characteristics of the RWP. In this paper we consider the RWP as a model of user mobility in networks that cater for a large geographical area - such as a city.

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cover image ACM Conferences
MSWiM '05: Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
October 2005
372 pages
ISBN:1595931880
DOI:10.1145/1089444
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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Published: 10 October 2005

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  1. mobility model
  2. validation

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MSWiM '05 Paper Acceptance Rate 48 of 165 submissions, 29%;
Overall Acceptance Rate 398 of 1,577 submissions, 25%

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  • (2018)Enhanced-Ant-AODV for optimal route selection in mobile ad-hoc networkJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2018.08.013Online publication date: Aug-2018
  • (2015)A Survey on Obstacle Modeling Patterns in Radio Propagation Models for Vehicular Ad Hoc NetworksArabian Journal for Science and Engineering10.1007/s13369-015-1600-640:5(1385-1407)Online publication date: 18-Feb-2015
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  • (2014)A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and ApplicationsIEEE Transactions on Mobile Computing10.1109/TMC.2013.15913:9(1911-1926)Online publication date: Sep-2014
  • (2014)Limits of Predictability for Large-Scale Urban Vehicular MobilityIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2014.232539515:6(2671-2682)Online publication date: Dec-2014
  • (2013)RECASTProceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems10.1145/2507924.2507950(327-334)Online publication date: 3-Nov-2013
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  • (2011)Modeling the Sociological Characteristic of Vehicle Mobility in Vehicular Sensor NetworkApplied Mechanics and Materials10.4028/www.scientific.net/AMM.48-49.71548-49(715-718)Online publication date: Feb-2011
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