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Association control algorithms for handoff frequency minimization in mobile wireless networks

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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.

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Notes

  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. 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. 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. 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{m, n, T}; the exponent here can be made arbitrarily large by appropriately setting the constant involved in the \(O(\cdot)\) notation.

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Authors and Affiliations

  1. IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532, USA

    Minkyong Kim, Srinivasan Parthasarathy & Dimitrios Pendarakis

  2. Nokia Research Center, Beijing, China

    Zhen Liu

  3. Nokia Research Center, 955 Page Mill Road, Palo Alto, CA, 94304, USA

    Hao Yang

Authors
  1. Minkyong Kim
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  2. Zhen Liu
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  3. Srinivasan Parthasarathy
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  4. Dimitrios Pendarakis
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  5. Hao Yang
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Corresponding author

Correspondence to Srinivasan Parthasarathy.

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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

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