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Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat

In wireless local area network (WLAN), the primary concern is Quality of Service (QoS) support that aims to satisfy the diverse service requirements and to guarantee higher data rates allocation for different service classes. However, IEEE 802.1 In standard does not specify a scheduling algorithm to...

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Main Author: Naji Maqhat, Bakeel Hussein
Format: Thesis
Language:English
Published: 2016
Subjects:
Algorithms
Fuzzy logic
Online Access:https://ir.uitm.edu.my/id/eprint/38826/1/38826.pdf
https://ir.uitm.edu.my/id/eprint/38826/
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id my.uitm.ir.38826
record_format eprints
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Algorithms
Fuzzy logic
spellingShingle Algorithms
Fuzzy logic
Naji Maqhat, Bakeel Hussein
Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
description In wireless local area network (WLAN), the primary concern is Quality of Service (QoS) support that aims to satisfy the diverse service requirements and to guarantee higher data rates allocation for different service classes. However, IEEE 802.1 In standard does not specify a scheduling algorithm to guarantee QoS. The performance benefits of existing solutions in MAC layers often fall short of providing the QoS support, particularly, it is still experiencing additional access latency and bandwidth allocation disorder where errors occur, that leads flows backlogged. The aim of this thesis is to develop a fair and efficient packet scheduling and adaptive bandwidth allocation algorithms to support QoS for a diverse service class for A-MSDU aggregation in IEEE 802.1 In network. This thesis presents four main contributions for QoS provisioning that are robust, scalable, and can be successfully implemented in WLAN networks. The first contribution is the AMS scheduling algorithm. The aim is to satisfy QoS requirements for time sensitive applications by exploiting the A-MSDU attributes and adopting the idea of enabling selective retransmission in our scheduling algorithm to obtain aggregation with small size to support time-sensitive applications and enable prioritization according to the QoS requirements of the traffic classes. The second contribution is an efficient bandwidth allocation algorithm for A-MSDU aggregation called Adaptive Scheduling based Embedded Fuzzy (ASEF) system. ASEF system is fully dynamic with fuzzy logic based approach and adaptive deadline-based scheme for various service class traffics. The algorithm employs fuzzy logic control which is embedded in the scheduler. The function is to control and dynamically update the bandwidth required by the various service classes according to their respective priorities, maximum latency, and throughput. The third contribution is to handle the influence of network channel conditions for the transmission process called Dynamic Sensing Mechanism based embedded Fuzzy (DSMF) expert system. The DSMF is an intelligent based system approach to support selective retransmission process and to enhance the performance by means of sensing the network channel conditions and updating the transmission decision. The final contribution is an efficient selection mechanism scheme for contending stations to access the channel called an Access Channel Selection based Fuzzy (ACSF) expert system for WLAN. ACSF can guarantee QoS requirements by allowing the real-time station to occupy the medium channel ahead of the non-real-time. The simulation results show the AMS algorithm significantly improves the performance over RSA-MSDU and the standard for real-time traffic in terms of reducing average delay and packet loss up to 56% and 24% respectively. Improving AMS scheduling by introducing ASEF scheme to allocate bandwidth between real time and non real-time traffics. The simulation results show the ASEF algorithm significantly improves the performance of AMS algorithm for about 67% for non real-time traffic and about 10% for real time traffic in term of reducing packet loss ratio; and improve the system throughput up to 54%. The results obtained by ACNF shows that by taking into account the network condition and channel access in building the scheme would increase the performance by reducing the packet loss by 80% on average and increase the system throughput by 15% on average as compared to ASEF.
format Thesis
author Naji Maqhat, Bakeel Hussein
author_facet Naji Maqhat, Bakeel Hussein
author_sort Naji Maqhat, Bakeel Hussein
title Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
title_short Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
title_full Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
title_fullStr Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
title_full_unstemmed Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat
title_sort adaptive resource allocation algorithms with qos support based on network conditions using fuzzy logic system for ieee 802.11n networks / bakeel hussein naji maqhat
publishDate 2016
url https://ir.uitm.edu.my/id/eprint/38826/1/38826.pdf
https://ir.uitm.edu.my/id/eprint/38826/
_version_ 1709671362651488256
spelling my.uitm.ir.388262021-08-27T06:34:06Z https://ir.uitm.edu.my/id/eprint/38826/ Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat Naji Maqhat, Bakeel Hussein Algorithms Fuzzy logic In wireless local area network (WLAN), the primary concern is Quality of Service (QoS) support that aims to satisfy the diverse service requirements and to guarantee higher data rates allocation for different service classes. However, IEEE 802.1 In standard does not specify a scheduling algorithm to guarantee QoS. The performance benefits of existing solutions in MAC layers often fall short of providing the QoS support, particularly, it is still experiencing additional access latency and bandwidth allocation disorder where errors occur, that leads flows backlogged. The aim of this thesis is to develop a fair and efficient packet scheduling and adaptive bandwidth allocation algorithms to support QoS for a diverse service class for A-MSDU aggregation in IEEE 802.1 In network. This thesis presents four main contributions for QoS provisioning that are robust, scalable, and can be successfully implemented in WLAN networks. The first contribution is the AMS scheduling algorithm. The aim is to satisfy QoS requirements for time sensitive applications by exploiting the A-MSDU attributes and adopting the idea of enabling selective retransmission in our scheduling algorithm to obtain aggregation with small size to support time-sensitive applications and enable prioritization according to the QoS requirements of the traffic classes. The second contribution is an efficient bandwidth allocation algorithm for A-MSDU aggregation called Adaptive Scheduling based Embedded Fuzzy (ASEF) system. ASEF system is fully dynamic with fuzzy logic based approach and adaptive deadline-based scheme for various service class traffics. The algorithm employs fuzzy logic control which is embedded in the scheduler. The function is to control and dynamically update the bandwidth required by the various service classes according to their respective priorities, maximum latency, and throughput. The third contribution is to handle the influence of network channel conditions for the transmission process called Dynamic Sensing Mechanism based embedded Fuzzy (DSMF) expert system. The DSMF is an intelligent based system approach to support selective retransmission process and to enhance the performance by means of sensing the network channel conditions and updating the transmission decision. The final contribution is an efficient selection mechanism scheme for contending stations to access the channel called an Access Channel Selection based Fuzzy (ACSF) expert system for WLAN. ACSF can guarantee QoS requirements by allowing the real-time station to occupy the medium channel ahead of the non-real-time. The simulation results show the AMS algorithm significantly improves the performance over RSA-MSDU and the standard for real-time traffic in terms of reducing average delay and packet loss up to 56% and 24% respectively. Improving AMS scheduling by introducing ASEF scheme to allocate bandwidth between real time and non real-time traffics. The simulation results show the ASEF algorithm significantly improves the performance of AMS algorithm for about 67% for non real-time traffic and about 10% for real time traffic in term of reducing packet loss ratio; and improve the system throughput up to 54%. The results obtained by ACNF shows that by taking into account the network condition and channel access in building the scheme would increase the performance by reducing the packet loss by 80% on average and increase the system throughput by 15% on average as compared to ASEF. 2016-09 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/38826/1/38826.pdf ID38826 Naji Maqhat, Bakeel Hussein (2016) Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat. PhD thesis, thesis, Universiti Teknologi MARA.
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