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Stochastically Dominant Access Point Selection in Wireless Local Area Networks
The rapid increase in the number of WiFi-enabled devices leads to the challenge of access point (AP) selection in enterprise wireless local area networks (eWLANs). Default vendor AP selection implementation results in over- and under-utilization of APs when devices cluster, and providing stochastic QoS guarantees for different applications becomes a major challenge. To this end, in this dissertation, AP selection algorithms are devised. More specifically, a stochastic network calculus concept called “ϵ-effective service curve” is extended into “η-effective service curve”. It is proven that comparing the η-effective service curves of two APs is sufficient to select the AP providing better QoS. The proof does not require steady-state quantile observations as in previous available bandwidth estimation techniques, therefore, transient behaviors can be compared. Utilizing this result, two AP selection algorithms, named STochastically dominant Access point selection algoRithm (STAR) and Mean-Dominance-based STAR (MD-STAR), are developed. STAR uses first-order stochastic dominance (FSD) tools from decision theory to compare quantile performance of delay in an effort to provide stochastic QoS guarantees. MD-STAR utilizes the concept of mean dominance (MD) to select the AP, which provides good average case performance with less overhead. Extensive simulations show both algorithms outperform state-of-the-art AP selection solutions both on the delay performance and stability. To further improve user’s QoS, STAR/MD-STAR selector, which relies on the calculation of remaining airtime difference, is proposed to determine which protocol to activate.
Bai, Yu, "Stochastically Dominant Access Point Selection in Wireless Local Area Networks" (2018). ETD collection for University of Nebraska - Lincoln. AAI13420435.