Publication
Title
ABRAHAM : machine learning backed proactive handover algorithm using SDN
Author
Abstract
An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.
Language
English
Source (journal)
IEEE transactions on network and service management. - New York, N.Y.
Publication
New York, N.Y. : 2019
ISSN
1932-4537
DOI
10.1109/TNSM.2019.2948883
Volume/pages
16 :4 (2019) , p. 1522-1536
ISI
000502781000017
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 11.12.2019
Last edited 09.10.2023
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