A QoS Awareness Scheme Sustaining Seamless  Handover for Network Mobility

Hussein, Loay F.; Hashim, Aisha Hassan Abdalla; Habaebi, Mohamed Hadi; Hassan, Wan Haslinah

doi:10.17485/ijst/2016/v9is1/109313

Cited by 4 publications

(2 citation statements)

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“…Similarly, the per-hop behavior (PHB) in a packet is determined by the DiffServ inside the IP header [22]. The DiffServ field consists of a 6-bit of DSCP value, allowing for up to 64 different classes, each represented by a DSCP value, which is identical in number [23]. However, in real-world environments, PHB typically falls into the following categories: the default forwarding (DF), expediated forwarding (EF), and assured forwarding (AF) [24].…”

Section: Traffic Classification Differentiated Services and Quality O...mentioning

confidence: 99%

Machine Learning-based Enhanced Deep Packet Inspection for IP Packet Priority Classification with Differentiated Services Code Point for Advance Network Management

Khan,

Ibrahim

2024

JTEC

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In modern networking, the efficient prioritization and classification of network traffic is paramount to ensure optimal network performance and optimization. This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). The DSCP resides inside the Differentiated Services (DS) field of the Internet Protocol (IP) packet header in an OSI or TCP/IP model, which prioritizes different types of packets for forwarding to the router based on the attached payload. Similarly, DPI plays a crucial role in network management, enabling the identification of applications, services, and potential threats within the network traffic. In this study, various machine learning models, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Logistic Regression and ensemble models such as, XGBoost, AdaBoost were used to harness the capabilities of network packet classification based on DSCP. Detailed experimentation was conducted to evaluate their performance. The results show that AdaBoost demonstrated superior performance with an accuracy of around 89.91%, showcasing its ability to adapt the evolving network configurations and conditions while maintaining high classification accuracy on the IP packets. The random forest model also performed well, achieving an accuracy of 89.41%, making it a strong candidate for the DSCP classification in network transmission. This study has the potential to significantly improve how networks manage traffic, prioritize packets, and secure complex and dynamic network environments.

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Section: Traffic Classification Differentiated Services and Quality O...mentioning

confidence: 99%

Machine Learning-based Enhanced Deep Packet Inspection for IP Packet Priority Classification with Differentiated Services Code Point for Advance Network Management

Khan,

Ibrahim

2024

JTEC

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“…These researches suggest embedding QoS awareness concept in NEMO based networks. For example, authors in [12] exploit DiffServ depiction to pull off silky delivery of real-time transferring in heterogeneous network mobility. In [13], the paper proposes a scheme uses DiffServ model composite within NEMO to offer privilege.…”

Section: Introductionmentioning

confidence: 99%

QoS-aware Cross Layer Handover Scheme for High-Speed vehicles

Nashaat¹

2018

KSII TIIS

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High-Speed vehicles can be considered as multiple mobile nodes that move together in a large-scale mobile network. High-speed makes the time allowed for a mobile node to complete a handover procedure shorter and more frequently. Hence, several protocols are used to manage the mobility of mobile nodes such as Network Mobility (NEMO). However, there are still some problems such as high handover latency and packet loss. So efficient handover management is needed to meet Quality of Service (QoS) requirements for real-time applications. This paper utilizes the cross-layer seamless handover technique for network mobility presented in cellular networks. It extends this technique to propose QoS-aware NEMO protocol which considers QoS requirements for real-time applications. A novel analytical framework is developed to compare the performance of the proposed protocol with basic NEMO using cost functions for realistic city mobility model. The numerical results show that QoS-aware NEMO protocol improves the performance in terms of handover latency, packet delivery cost, location update cost, and total cost.

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