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The Rise of Smart Devices on Mobility in Wireless Networks

This paper explores the impact of smart devices on mobility in wireless networks. It discusses the difficulties of wireless networks, the importance of mobility studies, roaming issues, and previous research in this field. The paper also presents two topology models for mobile studies and concludes with the need for more actual data and realistic models.

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The Rise of Smart Devices on Mobility in Wireless Networks

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  1. The Rise of Smart Devices on Mobility in Wireless Networks Daniel Ramos CS 790G Fall 2010

  2. Definitions • Wired Network • Structure is fixed and rarely changes. • Network topology is the physical connection between devices and access points (APs). • Wireless Network • Structure is dynamic and changes often. • Network topology is the radio connection between devices and APs.

  3. Wireless Networks Difficulties • Typically lower bandwidth and quality of service than wired networks. • The environment can effect the radio signal to the AP. • Even if the connection is stable now the user might move! • If a user moves out of range of an AP, they have to be associated to a new one.

  4. Mobile Smart Devices • Highly portable devices becoming increasingly popular:

  5. Mobile Smart Devices • Examples: • Personal Digital Assistants (PDAs) • iPhone and other “smartphones” • iPad and other tablet PCs • Portable game consoles with wireless connectivity (e.g. Nintendo DS and Sony PSP)

  6. Why are Mobility Studies Important? • Quality of Service • Resource Reservation • Load Balancing • Bandwidth Utilization • Capacity Planning

  7. Roaming • Definition: Extension of connectivity service in a location that is different from the home location where the service was registered. • Cellular Networks • Moving between cell sites • Handoff or Handover • Cells are typically on the scale of kilometers • Computer Networks • Moving between access points • Typically on the scale meters

  8. Roaming Problems • Maximum number of clients already reached • Different providers own different access points • Different or worse capabilities between access points

  9. Previous Studies • Some focused on simulated mobility models • Random Walks • Unrealistic movements • Totally opposite directions • No real-world obstacles • Some focused on real data • Low mobility devices • Laptops

  10. Modeling Roaming in Large-scale Wireless Networks using Real Measurements [1] • Did not focus on any one device type • Modeled roaming as a graph • Analyzed the degree of connectivity • Focused on the access points

  11. Data Collection • 3 phases over 7 months • Recorded syslog events from APs • Authentications/Deauthentications • Associations/Dissociations • Roam between APs • Mostly laptops, but a few PDAs

  12. Modeling Roaming as a Graph • GT = (VT, ET) • Named a “roaming graph” • Node is an APs • Edge is at least one client transition between i and j • “Crosspoint” APs • Accounting for “wiggling”

  13. Degree of Connectivity • Only 24% of clients were “mobile” • Can be modeled as a negative binomial distribution

  14. Other Results • Negative correlation between AP distance and transitions • New APs decrease “crosspoints” • New APs might make wiggling worse

  15. Problems • Only low mobility devices were captured • Meaningful stastics for future devices?

  16. Access and mobility of wireless PDA users[2] • Focused on PDA devices • Characterized PDA mobility and access patterns • Developed 2 topology models for mobile studies

  17. Data Collection • Collected over a single semester • Background application • Sent AP association information and signal strengh of all detected APs.

  18. Mobility • Over twice as mobile as than laptops

  19. Evolutionary Topology Model • Based on observed network proximity • Nodes are users • Edges are if users could “communicate” • If they both could connect a common AP.

  20. Campus Waypoint Model • Based on the Random waypoint model • Used trace data for mobility vectors instead of relying on random or synthetic models. • Based on evolving set of sensed APs. • Determines velocity and direction.

  21. Results • Evolutionary • Users created “islands” of disconnected graphs • Average degree of 4 • Waypoint • Only an average 11% are mobile at the same time • Users only move a 2.2 MPH • Appear and disappear frequently

  22. Problems • Higher then normal drop-out rate • Drained batteries erased trace application • Wireless card was bulky • Non-volunteer sample group • Median of one wireless session per day

  23. Conclusion • Studying mobility is important to network protocol evolution • Highly mobile devices are already changing both computer and cellular network usage • More actual data and realistic models are needed

  24. References • [1] M. Papadopouli, M. Moudatsos, and M. Karaliopoulos, “Modeling Roaming in Large-scale Wireless Networks using Real Measurements,” Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, pp. 536-541, June 2006 • [2] M. McNett and G. M. Voelker, “Access and mobility of wireless PDA users,” ACM SIGMOBILE Mobile Computing and Communications Review, Volume 9, Issue 2 (April 2005).

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