Article

Mobility prediction using future knowledge

Authors:

Michael H. Sun,

Douglas M. BloughAuthors Info & Claims

MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

Pages 235 - 239

https://doi.org/10.1145/1298126.1298167

Published: 23 October 2007 Publication History

Get Access

Abstract

Anticipating user mobility can be a critical feature for today's mobile systems. We introduce a novel location predictor which incorporates knowledge of a user's potential future locations to improve prediction accuracy. Such future knowledge is often available through contextual sources such as a user's calendar, e-mail, or instant messaging conversations. Simulation results show that our future knowledge leveraging location predictor can improve prediction accuracy by 3% to 95% over history-only Markov predictors, depending on the amount of future knowledge that is available and the type of mobility exhibited by users.

References

[1]

A. Bhattacharya and S. Das. LeZi-update: an information-theoretic approach to track mobile users in PCS networks. Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, pages 1--12, 1999.

Digital Library

Google Scholar

[2]

J. Biesterfeld, E. Ennigrou, and K. Jobmann. Location Prediction in Mobile Networks with Neural Networks. Proc. IWANNT, 97:207--214.

Google Scholar

[3]

C. Cheng, R. Jain, and E. van den Berg. Location prediction algorithms for mobile wireless systems. Wireless internet handbook: technologies, standards, and application table of contents, pages 245--263, 2003.

Digital Library

Google Scholar

[4]

D. Kotz, T. Henderson, and I. Abyzov. CRAWDAD trace dartmouth/campus/movement/infocom04 (v. 2004-08-05). Downloaded from http://crawdad.cs.dartmouth.edu, Aug. 2004.

Google Scholar

[5]

J. Lee and J. Hou. Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application. Proceedings of the seventh ACM international symposium on Mobile ad hoc networking and computing, pages 85--96, 2006.

Digital Library

Google Scholar

[6]

G. Liu and G. Maguire. A class of mobile motion prediction algorithms for wireless mobile computing and communications. Mobile Networks and Applications, 1(2):113--121, 1996.

Digital Library

Google Scholar

[7]

L. Song, D. Kotz, R. Jain, and X. He. Evaluating location predictors with extensive Wi-Fi mobility data. In Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), volume 2, pages 1414--1424, March 2004.

Crossref

Google Scholar

[8]

F. Yu and V. Leung. Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks. Computer Networks, 38(5):577--589, 2002.

Digital Library

Google Scholar

[9]

J. Ziv and A. Lempel. Compression of individual sequences via variable-rate coding. Information Theory, IEEE Transactions on, 24(5):530--536, 1978.

Digital Library

Google Scholar

Cited By

View all

Sadeghian PHan MHåkansson JZhao M(2024)Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking dataTransportmetrica B: Transport Dynamics10.1080/21680566.2024.233603712:1Online publication date: 4-Apr-2024
https://doi.org/10.1080/21680566.2024.2336037
Wang ZSun PHu YBoukerche ABoukerche AGiannelli CSun Z(2022)A Novel Mixed Method of Machine Learning Based Models in Vehicular Traffic Flow PredictionProceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3551659.3559047(95-101)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1145/3551659.3559047
Lee JLee SRim MKang C(2020)System-Level Spatiotemporal Offloading With Inter-Cell Mobility Model for Device-to-Device (D2D) Communication-Based Mobile Caching in Cellular NetworkIEEE Access10.1109/ACCESS.2020.29805608(51570-51581)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2980560
Show More Cited By

Index Terms

Mobility prediction using future knowledge
1. Information systems
  1. Information systems applications

Recommendations

Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data

Location is an important feature for many applications, and wireless networks may serve their clients better by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with ...
A data mining approach for location prediction in mobile environments

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next intercell movement of a mobile user in a Personal ...
A Hybrid Mobility Prediction (HMP) strategy for PCS networks

Recently, several high-speed applications have been emerged for personal communication service (PCS) networks. Therefore, locating mobile terminals (MTs) as they move from one place to another is the key to provide continuous services with unrestricted ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

October 2007

422 pages

ISBN:9781595938510

DOI:10.1145/1298126

General Chair:
Carla-Fabiana Chiasserini
Politecnico di Torino, Italy
,
Program Chairs:
Nael Abu-Ghazaleh
State University of New York--Binghamton, NY
,
Sotiris Nikoletseas
University of Patras and CTI, Greece

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

MSWiM07

Sponsor:

MSWiM07: 10th International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 22 - 26, 2007

Crete Island, Chania, Greece

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
377
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sadeghian PHan MHåkansson JZhao M(2024)Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking dataTransportmetrica B: Transport Dynamics10.1080/21680566.2024.233603712:1Online publication date: 4-Apr-2024
https://doi.org/10.1080/21680566.2024.2336037
Wang ZSun PHu YBoukerche ABoukerche AGiannelli CSun Z(2022)A Novel Mixed Method of Machine Learning Based Models in Vehicular Traffic Flow PredictionProceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3551659.3559047(95-101)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1145/3551659.3559047
Lee JLee SRim MKang C(2020)System-Level Spatiotemporal Offloading With Inter-Cell Mobility Model for Device-to-Device (D2D) Communication-Based Mobile Caching in Cellular NetworkIEEE Access10.1109/ACCESS.2020.29805608(51570-51581)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2980560
Karimzadeh MZhao ZGerber FBraun T(2018)Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2018.8589126(200-207)Online publication date: Oct-2018
https://doi.org/10.1109/WiMOB.2018.8589126
Abdel-Halim IFahmy H(2018)Prediction-based protocols for vehicular Ad Hoc NetworksComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2017.10.009130:C(34-50)Online publication date: 15-Jan-2018
https://dl.acm.org/doi/10.1016/j.comnet.2017.10.009
Hadachi ABatrashev OLind ASinger GVainikko E(2014)Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm2014 IEEE Intelligent Vehicles Symposium Proceedings10.1109/IVS.2014.6856442(1049-1054)Online publication date: Jun-2014
https://doi.org/10.1109/IVS.2014.6856442
Daoud MAyesh AAl-Fayoumi MHopgood A(2014)Location Prediction Based on a Sector Snapshot for Location-Based ServicesJournal of Network and Systems Management10.1007/s10922-012-9258-922:1(23-49)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1007/s10922-012-9258-9
Khan AJayarajah KHan DMisra ABalan RSeshan SChu HHuang PChoudhury RZhao F(2013)CAMEOProceeding of the 11th annual international conference on Mobile systems, applications, and services10.1145/2462456.2464436(125-138)Online publication date: 26-Jun-2013
https://dl.acm.org/doi/10.1145/2462456.2464436
Lee HYoung-uk Chung Choi Y(2013)A seamless handover scheme for IEEE WAVE networks based on multi-way proactive caching2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN.2013.6614841(356-361)Online publication date: Jul-2013
https://doi.org/10.1109/ICUFN.2013.6614841
Pushpa PSneha SAgrawal R(2012)User Informatics Optimized Search and Retrieval-Congestion Avoidance Scheme for 4G NetworksCommunications and Network10.4236/cn.2012.4302604:03(219-226)Online publication date: 2012
https://doi.org/10.4236/cn.2012.43026
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data

A data mining approach for location prediction in mobile environments

A Hybrid Mobility Prediction (HMP) strategy for PCS networks