عرض بسيط للتسجيلة

المؤلفKhan, Muhammad Asif
المؤلفHamila, Ridha
المؤلفGastli, Adel
المؤلفKiranyaz, Serkan
المؤلفAl-Emadi, Nasser Ahmed
تاريخ الإتاحة2022-11-23T11:25:32Z
تاريخ النشر2022
اسم المنشورJournal of Network and Systems Management
المصدرScopus
المصدر2-s2.0-85137077870
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s10922-022-09684-2
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36629
الملخصDevice mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively. 2022, The Author(s).
اللغةen
الناشرSpringer
الموضوعAccess point selection
Cognitive networks
Handover
Machine learning
Throughput
Wi-Fi
العنوانML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
النوعArticle
رقم العدد4
رقم المجلد30
dc.accessType Open Access


الملفات في هذه التسجيلة

Thumbnail

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة