Association control algorithms for handoff frequency minimization in mobile wireless networks

Abstract

As mobile nodes roam in a wireless network, they continuously associate with different access points and perform handoff operations. Frequent handoffs performed by a mobile device may have undesirable consequences, as they can cause interruptions for interactive applications and increase the energy usage of mobile devices. While existing approaches to this issue focus entirely on improving the latency incurred by individual handoffs, in this paper, we initiate a novel approach to association control of mobile devices with the goal of reducing the frequency of handoffs for mobile devices. We study the handoff minimization problem across multiple dimensions: offline versus online where the complete knowledge of mobility patterns of users is known in advance or unknown respectively; capacity constrained versus unconstrained access points, which imposes limits on the number of mobile devices which could be associated with a given access point at any given point in time; group mobility versus arbitrary mobility of users, which are contrasting ways to model the mobility patterns of the mobile users. We consider various combinations of the above dimensions and present the following: (1) optimal algorithms, (2) provably-good online and offline approximation algorithms, (3) complexity (NP-Completeness) results, and (4) a practical heuristic which is demonstrated to work well on real network traces.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    A preliminary conference version of this work appeared in IEEE Infocom 2009 [7]. This journal version includes several significant additions: the entire set of results in Sect. 5 which pertains to association control with AP capacity constraints is a new addition; further, we have made substantial improvements to the introduction and motivation sections.

  2. 2.

    In other words, before invoking the association control mechanism, the user may first filter out those APs with poor performance, measured by signal strengths, current loads, etc. However, such application-specfic filtering policies are out of the scope in this work.

  3. 3.

    Much of our algorithms and analysis work under other objectives as well: e.g., minimize the weighted sum of handoffs, or minimize the maximum number of handoffs experienced by a user. We will use this global objective throughout the paper for ease of exposition.

  4. 4.

    High probability in this theorem refers specifically to the following: the probability that the violation of capacity values exceed \(O(\sqrt{\max\{\log{m},\log{n},\log{T}\}})\) is inverse exponential in max{mnT}; the exponent here can be made arbitrarily large by appropriately setting the constant involved in the \(O(\cdot)\) notation.

References

  1. 1.

    Balachandran, A., Bahl, P., & Voelker, G. (2002). Hot-spot congestion relief in public-area wireless networks. SIGCOMM Computer Communication Review, 32, 59.

    Article  Google Scholar 

  2. 2.

    Bejerano, Y., Han, S.-J., & Li, L. (2004, September). Fairness and load balancing in wireless LANs using association control. In Proceedings of ACM Mobicom, Philadelphia, PA, USA.

  3. 3.

    Cisco Systems Inc. (2006). Aironet 802.11 a/b/g WLAN client adapter data sheet.

  4. 4.

    Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wallach, D. S., & Kavraki, L. E. (2002, September). Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th ACM international conference on mobile computing and networkign (MOBICOM), Philadelphia, PA.

  5. 5.

    Henderson, T., Kotz, D., & Abyzov, I. (2004, September). The changing usage of a mature campus-wide wireless network. In Proceedings of the 10th annual international conference on mobile computing and networking (MobiCom) (pp. 187–201). ACM Press.

  6. 6.

    Jardosh, A., Mittal, K., Ramachandran, K., Belding, E., & Almeroth, K. (2006, September). IQU: Practical queue-based user association management for WLANs. In Proceedings of ACM Mobicom, Los Angeles, CA, USA.

  7. 7.

    Kim, M., Liu, Z., Parthasarathy, S., Pendarakis, D. E. & Yang, H. (2008). Association control in mobile wireless networks. In IEEE INFOCOM (pp. 1256–1264).

  8. 8.

    King, T., Kaenselmann, T., Kopf, S., & Effelsberg, W. (2006). Overhearing the wireless interface for 802.11-based positioning systems. In Proceedings of the 5th annual IEEE international conference on pervasive computing and communications (PerCom’07) (pp. 145–150). New York: White Plains.

  9. 9.

    Klein, M. (1967). A primal method for minimal cost flows with applications to the assignment and transportation problems. Management Science, 14, 205–220.

    MATH  Article  Google Scholar 

  10. 10.

    Kleinberg, J., & Tardos, E. (2005). Algorithm design. Boston, MA: Addison-Wesley/Longman.

    Google Scholar 

  11. 11.

    Kolen, A. W. J., Papadimitriou, C. H., Lenstra, J. K., & Spieksma, F. C. R. (2007). Interval scheduling: A survey. Naval Research Logistics, 54, 530–543.

    MathSciNet  MATH  Article  Google Scholar 

  12. 12.

    Li, B. (2002). On increasing service accessibility and efficiency in wireless ad-hoc networks with group mobility. Wireless Personal Communications—Special Issue on Multimedia Networking and Enabling Radio Technologies, 21(1), 105–123.

    Google Scholar 

  13. 13.

    MaNett, M., & Voelker, G. M. (2005). Access and mobility of wireless PDA users. Mobile Computing Communications Review, 9, 40–55.

    Article  Google Scholar 

  14. 14.

    Mishra, A., Shin, M., & Arbaugh, W. (2004, March). Context caching using neighbor graphs for fast handoffs in a wireless network. In Proceedings of IEEE Infocom, Hong Kong, China.

  15. 15.

    Ozdaglar, A. E. & Bertsekas, D. P. (2003). Optimal solution of integer multicommodity flow problems with application in optical networks. In Proceedings of symposium on global optimization.

  16. 16.

    Pack, S., & Choi, Y. (2002, August). Fast inter-AP handoff using predictive authentication scheme in a public wireless LAN. In Proceedings of IEEE networks conference, Atlanta, GA.

  17. 17.

    Raghavan, P., & Thompson, C. D. (1987). Randomized rounding: A technique for provably good algorithms and algorithmic proofs. Combinatorica, 7(4), 365–374.

    MathSciNet  MATH  Article  Google Scholar 

  18. 18.

    Ramani, I., & Savage, S. (2005, March). SyncScan: Practical fast handoff for 802.11 infrastructure networks. In Proceedings of IEEE Infocom, Miami, FL.

  19. 19.

    Shin, M., Mishra, A., & Arbaugh, W. (2004, June). Improving the latency of 802.11 hand-offs using neighbor graphs. In Proceedings of ACM MobiSys, Boston, MA.

  20. 20.

    Symbol Technologies. (2006). Wireless networker CF radio card data sheet.

  21. 21.

    Tsai, T.-C., & Lien, C.-F. (2003). IEEE 802.11 hot-spot load balance and QoS-maintained seamless roaming. In Proceedings of national computer symposium (NCS).

  22. 22.

    Wu, H., Tan, K., Zhang, Y., & Zhang, Q. (2007). Proactive scan: Fast handoff with smart triggers for 802.11 wireless lan. In INFOCOM (pp. 749–757).

  23. 23.

    Zhang, Y., Liu, Y., Xia, Y., & Huang, Q. (2007). Leapfrog: Fast, timely wifi handoff. In GLOBECOM (pp. 5170–5174).

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Srinivasan Parthasarathy.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kim, M., Liu, Z., Parthasarathy, S. et al. Association control algorithms for handoff frequency minimization in mobile wireless networks. Wireless Netw 18, 535–550 (2012). https://doi.org/10.1007/s11276-012-0417-4

Download citation

Keywords

  • Wireless networks
  • Handoff
  • Algorithms
  • Approximation ratio
  • Competitive ratio
  • Optimization