Mobility aware load balancing using Kho–Kho optimization algorithm for hybrid Li-Fi and Wi-Fi network

Alharbi, Meshal; Neelakandan, S.; Gupta, Sachi; Saravanakumar, R.; Kiran, Siripuri; Mohan, A.

doi:10.1007/s11276-022-03225-0

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Anytime a status change takes place, an SDN controller dispatches wireless devices from overloaded APs to underloaded ones [45]. However, because a number of other factors affect channel conditions, focusing simply on each APs load is not the best approach.…”

Section: Ap-level Load Balancingmentioning

confidence: 99%

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

Manzoor,

Kayani,

Ali

et al. 2023

IEEE Access

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With the rise in smart devices, challenges such as traffic management particularly in the indoor scenarios have also increased. Wireless technologies seek to address these needs by using new radio access technologies that offer faster connectivity and access to the Internet. While 4G and 5G provide faster connectivity outdoor, Wi-Fi still remains the dominant technology in indoor scenarios due to its low cost and easy deployment. However, the IEEE 802.11 standard does not address the fairness of load in user-access point (AP) associations, resulting in unequal user distributions and subpar resource utilization. Densification has been employed in the past to address the aforementioned issue, but it is costly as it requires more hardware. Load balancing is necessary in high-density Wi-Fi networks in order to guarantee quality of service (QoS). To avoid the densification and issues related to load balancing at the APs this paper introduces tetra indicator Wi-Fi associations (TiWA), a combined SDN-based user association and channel assignment solution for Wi-Fi networks. TiWA considers multi-criteria to make better association decisions. The wireless stations from an overloaded OpenFlow enabled AP (OAP) are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI), throughput and channel occupancy. Extensive experimentations on 5G EmPOWER platform show that TiWA surpasses previous research methods based on load balancing in terms of aggregated throughput by 39%, offering superior level of load fairness and supporting greater numbers of users and traffic prior to hitting the point at which densification is required.

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Section: Ap-level Load Balancingmentioning

confidence: 99%

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

Manzoor,

Kayani,

Ali

et al. 2023

IEEE Access

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“…DCF access protocol in this emulator made it more difficult to conduct studies on uplink network throughput when several users were sharing a single channel. SD-Wi-Fi prototype systems are typically built using software-defined wireless network testbeds [35]. It should be noted that the testbeds are only suitable for implementing network layout with small-scale experiments and are not applicable for highly dense Wi-Fi network scenarios with a significant number of users and APs.…”

Section: Load Balancing In Data Planementioning

confidence: 99%

An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks

Manzoor,

Mazhar,

Binaris

et al. 2023

Symmetry

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In dense Wi-Fi networks, achieving load balancing is critical to optimize network utilization and provide equitable network consumption among the users. Traditional Wi-Fi networks have issues in attaining effective load balancing. Software-Defined Networking (SDN) has presented a viable solution by isolating the data plane and control plane, enabling more agile and cost-effective networks. In this paper we put forward an Adaptive Symmetrical Load Balancing (ASLB) scheme to ensure fairness of load symmetry in Software Defined Wi-Fi Networks (SD-Wi-Fi), while also optimizing the flows transition process using an Analytical Hierarchal Process (AHP). User activity is monitored by access points (APs), which operate under OpenFlow standards. Three essential features, packet volume, packet category and delay hindrance, are used for flow assignment to various controllers. The controllers are arranged in two tiers, universal and regional controllers. The universal controller (UC) handles the workload statistics of regional controllers (RC) in the form of clusters. Extensive simulations using OMNeT++ simulator are performed. The performance parameters taken into consideration are throughput, delay, packet loss rate, network transition count and workload distribution. Our findings demonstrate that the ASLB technique effectively optimizes the network utilization and ensures equitable network consumption among the end users. The proposed scheme outperforms the Mean Probe Delay scheme (MPD), Channel Measurement-based Access Selection scheme (CMAS), Received Signal Strength Indicator-based scheme (RSSI) and Distributed Antenna Selection scheme (DASA), being 40% higher in throughput and 25% lower in delay.

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“…They perform better than the RNN prototype overall. Therefore, we employ stacked RNN to project upcoming measures [36]. Network architecture…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

Swathi,

Narsimha

2023

IJRITCC

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The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.

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Mobility aware load balancing using Kho–Kho optimization algorithm for hybrid Li-Fi and Wi-Fi network

Cited by 7 publications

References 28 publications

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

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