Behavior-based mobility prediction for seamless handoffs in mobile wireless networks
- 593 Downloads
The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms.
KeywordsMobility prediction Fast handoffs WLANs WMNs
The work described in this paper was supported in part by the NSF under Grant CNS-0831853 and CNS-0821319, and Korean NRF under WCU Grant R31-2008-000-10100-0.
- 1.MetroFi Portland Free Wi-Fi. Online. Available: http://www.metrofiportland.com.
- 2.SeattleWireless. Online. Available: http://SeattleWireless.net.
- 3.NYCwireless. Online. Available: http://NYCwireless.net.
- 4.Rooftop@Media. Online. Available: http://rooftops.media.mit.edu.
- 5.Draft Standard for Information Technology— Telecommunications and Information Exchange Between Systems—LAN/MAN Specific Requirement - Part 11:Wireless LAN Medium Access Control and Physical Layer Specifications: Amendment: ESS Mesh Networking, IEEE Unapproved draft Std. P802.11s/D1.02, Mar 2007.Google Scholar
- 6.Jovanov, E., Milenkovic, A., Otto, C., & de Groen, P. C. (2005). A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of Neuroengineering Rehabiltation (Online). Mar 2005.Google Scholar
- 7.Ashbrook, D., & Starner, T. (2006). Poster: Slope: A system for rapid deployment of vanet communication protocols. In The international conference on mobile systems, applications, and services (MOBISYS). Jun 2006.Google Scholar
- 8.Ramani, I., & Savage, S. (2005). Syncscan: Practical fast handoff for 802.11 infrastructure networks. In IEEE INFOCOM (pp. 675–684). Mar 2005.Google Scholar
- 9.ITU-T recommendation G.114, (1993). International Telecommunication Union, Tech. Rep.Google Scholar
- 10.Brik, V., Mishra, A., & Banerjee, S. (2005). Eliminating handoff latencies in 802.11 WLANs using multiple radios: Applications, experience, and evaluation. In Internet measurement conference (IMC) (pp. 27–27). Oct 2005.Google Scholar
- 11.Waharte, S., Ritzenthaler, K., & Boutaba, R. (2004). Selective active scanning for fast handoff in WLAN using sensor networks. In Mobile and wireless communications networks (MWCN) (pp. 59–70). Oct 2004.Google Scholar
- 12.Shin, M., Mishra, A., & Arbaugh, W. A. (2004). Improving the latency of 802.11 hand-offs using neighbor graphs. In The international conference on mobile systems, applications, and services (MOBISYS) (pp. 70–83). Jun 2004.Google Scholar
- 13.Shin, S., Forte, A. G., Rawat, A. S., & Schulzrinne, H. (2004). Reducing mac layer handoff latency in IEEE 802.11 wireless LANs. In ACM international workshop on mobility management and wireless access (MOBIWAC) (pp. 19–26). Sep 2004.Google Scholar
- 14.Wanalertlak, W., & Lee, B. (2007). Global path-cache technique for fast handoffs in WLANs. In International conference on computer communications and networks (ICCCN) (pp. 45–50). Aug 2007.Google Scholar
- 15.Local and Metropolitan Area Network, Part 11: Wireless LAN Medium Access Control and Physical Layer Specifications, IEEE Std. 802.11, (2007).Google Scholar
- 17.Shumway, R. H., & Stoffer, D. S. (2006). Time series analysis and its applications: With R examples, 2nd ed. New York: Springer.Google Scholar
- 18.Ieee 802.11f standard: Recommend practice for multi-vendor access point interpretability via an inter-access point protocol. Online. Available: http://grouper.ieee.org/groups/802/11/private/Draft_Standards/11f/802.11f-D3.1.pdf.
- 19.Yoon, J., Noble, B. D., Liu, M., & Kim, M. (2006). Building realistic mobility models from coarse-grained traces. In The international conference on mobile systems, applications, and services (MOBISYS) (pp. 177–190). Jun 2006.Google Scholar
- 20.Umedu, T., Urabe, H., Tsukamoto, J., Sato, K., & Higashino, T. H. T. (2006). A manet protocol for information gathering from disaster victims. In Fourth annual IEEE international conference on pervasive computing and communications workshops (pp. 442–447). Mar 2006.Google Scholar
- 21.Broch, J., Maltz, D. A., Johnson, D. B., Chun Hu, Y., & Jetcheva, J. (1998). A performance comparison of multi-hop wireless ad hoc network routing protocols. In ACM international conference on mobile computing and networking (MobiCom) (pp. 85–97). Oct. 1998.Google Scholar
- 22.Network simulator (ns2). Online. Available: http://www.isi.edu/nsnam/ns.
- 23.Amit’s thoughts on path-finding and A*. Online. Available: http://theory.stanford.edu/amitp/GameProgramming.
- 24.Rappaport, T. S. (2002). Wireless communications: Principles and practice, 2nd ed. NJ: Prentice Hall.Google Scholar
- 25.Atheros ar5002x 802.11a/b/g universal WLAN solution. Online. Available: http://www.atheros.com/pt/AR5002XBulletin.htm.
- 26.MadWIFI_0.9.2. Online. Available: http://www.madwifi.org.
- 27.Katsaros, D., Nanopoulos, A., Karakaya, M., Yavas, G., Ulusoy, U., & Manolopoulos, Y. (2003). Clustering mobile trajectories for resource allocation in mobile environments, ser. Lecture notes in computer science, vol. 2779/2003. New York: Springer. Sep. 2003.Google Scholar
- 29.Kim, T.-H., Yang, Q., Lee, J.-H., Park, S.-G., & Shin, Y.-S. (2007). A mobility management technique with simple handover prediction for 3G LTE systems. In Vehicular technology conference (VTC) (pp. 259–263). Jun 2007.Google Scholar
- 31.Wu, H.-K., Jin, M.-H., Horng, J.-T., & Ke, C.-Y. (2001). Personal paging area design based on mobile’s moving behaviors. IEEE INFOCOM, 1, 21–23.Google Scholar
- 33.Song, L., Deshpande, U., Kozat, U. C., Kotz, D., & Jain, R. (2006). Predictability of WLAN mobility and its effects on bandwidth provisioning. In IEEE INFOCOM (pp. 1–13). Apr 2006.Google Scholar
- 34.Franois, J.-M. (2007). Performing and making use of mobility prediction. Ph.D. dissertation, University of Lige.Google Scholar
- 36.You, C.-W., Chen, Y.-C., Chiang, J.-R., Huang, P., Chu, H.-H., & Lau, S.-Y. (2006). Sensor-enhanced mobility prediction for energy-efficient localization. Sensor and Ad Hoc Communications and Networks (SECON), 2, 565–574.Google Scholar
- 37.Pack, S., & Choi, Y. (2004). Fast handoff scheme based on mobility prediction in public wireless LAN systems. IEE Proceedings Communications, 151(5) 489–495.Google Scholar
- 39.Chan, J., Zhou, S., & Seneviratne, A. (1997). A hybrid handoff scheme with prediction enhancement for wireless ATM network. In IEEE Asia Pacific Conference on Communications (APCC), Dec. 1997, pp. 494–498.Google Scholar
- 40.Chan, J., Zhou, S., & Seneviratne, A. (1998). A QoS adaptive mobility prediction scheme for wireless networks. In IEEE conference and exhibition global telecommunications conference (GLOBECOM) (pp. 1414–1419). Nov 1998.Google Scholar
- 41.Ma, W., & Fang, Y. (2002). A new location management strategy based on user mobility pattern for wireless networks. In IEEE conference on local computer networks (LCN) (pp. 451–457). Nov 2002.Google Scholar
- 44.Sricharan, M., Vaidehi, V., & Arun, P. (2006). An activity based mobility prediction strategy for next generation wireless networks. In Wireless and optical communications networks: The next generation of internet (WOCN). Apr 2006.Google Scholar
- 46.Ashbrook, D., & Starner, T. (2002). Learning significant locations and predicting user movement with GPS. In IEEE international symposium on wearable computers (ISWC) (pp. 101–108). Oct 2002.Google Scholar