Smart mobility management in 5G heterogeneous networks

Omheni, Nouri; Bouabidi, Imen El; Gharsallah, Amina; Zarai, Faouzi; Obaidat, Mohammad S.

doi:10.1049/iet-net.2017.0208

Cited by 29 publications

(18 citation statements)

References 18 publications

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“…The designed method failed to consider the traffic for minimizing the packet loss. A novel handover mechanism based on MIH/PMIPv6 was developed in [14] to perform the vertical handover for seamless mobility. Though the mechanism minimizes the packet loss, the transmission delay was not reduced.…”

Section: Related Workmentioning

confidence: 99%

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

D.¹,

Dr.²

2020

INDJCSE

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Seamless mobility management is an ability to provide the various services during the communication in wireless heterogeneous networks. Due to the random mobility of the mobile terminals, the connectivity between different mobile devices gets lost. In order to provide the lossless connectivity between the mobile devices, the handover from the point of current attachment to another point is necessary. To improve the Seamless mobility management and traffic control, an efficient model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is introduced in the heterogeneous network. When a mobile node in the network moves out of its communication range, the signal strength of the nodes is calculated. Based on the signal strength estimation, the Generalized Light Gradient Boost Decision Tree classifier categorizes the mobile nodes into the weak and strong signal strength with the threshold value. The boosting algorithm initially constructs' weak learners i.e. binary decision tree to identify the weak signal strength of the mobile node. Then the ensemble classifier combines the results of weak learners and minimizes the generalization error. This helps to perform the handover only with the weak signal strength of the node resulting in minimizes the redundant handover. In addition, the weak signal strength of the mobile node from the current attachment point handover towards the nearest available attachment point to improve the continuous data delivery. Followed by, bandwidth availability is measured for reducing the packet loss due to thenetwork traffic resulting in improves the seamless data delivery between the nodes. The simulation is carried out to evaluate the performance of the GLGBDT-TASM model with two related approaches. The results show that the GLGBDT-TASM model effectively improved traffic-aware seamless mobility in a heterogeneous network with minimum delay and packet loss as well as a higher data delivery rate as compared to state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

D.¹,

Dr.²

2020

INDJCSE

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“…Hence, a good approach of mobility management is essential in preserving the Quality of Service (QoS) to user. Mobility management in heterogeneous wireless network has emerged and researched over the years [1]- [3]. One of the important parts in mobility management is how to manage the handover process when the mobile node changes or move away from current service network to another service network without disrupting the communication connectivity.…”

Section: Introductionmentioning

confidence: 99%

Conceptual mobility model of vertical handover decision in heterogeneous networks

Arbain¹,

Kasiran²

2019

IJEECS

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In heterogeneous network, maintaining seamless connectivity needs excessive efforts from various aspects such as network availability and mobile node reliability. Presently, a vertical handover management is a practical approach in facilitating the service continuity for mobile users. Many researches have been conducted in this area by considering performance improvement in delay, latency, and overhead. Preserving the Quality of Services (QoS) based on user mobility and pattern movement during handover decision has become an important aspect in vertical handover management. This paper presents the conceptual mobility model of vertical handover decision in heterogeneous network. Hence, several researches in vertical handover decision management has been reviewed regarding the issues on the vertical handover decision algorithms such as RSS Based Algorithm, MADM Based Algorithm and Intelligence Based Algorithm. This paper highlights the current decision algorithms that integrate the traditional methods with intelligence algorithm for better optimization. In decision parameters, the user mobility pattern can be importance in terms of direction randomness and mobility speed. Hence, a conceptual mobility-awareness model for vertical handover are been proposed in targeting some improvement of handover performance.

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“…In the context of MCDM approach we recognize numerous algorithms such as Analytic Hierarchy Process (AHP), Distance to Ideal Alternative (DIA), Analytic Network Process (ANP) to calculate the weight criteria, likewise others algorithms to classifying alternatives suchlike Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), ELimination and Choice Expressing Reality (ELECTRE), Grey Relational Analysis (GRA). Most of these algorithms realized with ABC [2] (Always-Best-Connection), articulating on performance to choose the best network destination, but with an inefficiency use of resources. To deal with this problem we suggest an algorithm based on ASC (Always-Suitable-Connection) to employ wireless networking resources effectively.…”

Section: Introductionmentioning

confidence: 99%

Improving Modified Grey Relational Method for Vertical Handover in Heterogeneous Networks

Chattate¹,

Khaïli²,

Bakkoury³

2019

ijacsa

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With the advent of next-generation wireless network technologies, vertical handover has become indispensable to keep the mobile user always best connected (ABC) in a heterogeneous environment, especially the significant number of multimedia applications that require good quality of service (QoS) for users. To handle this issue, an improvement of modified Grey Relational Analysis (MGRA) to select the Always-Suitable-Connection (ASC) network has been proposed. Then, Fuzzy analytic hierarchy process (FAHP) method has been used to determine the weight of criteria. In order to validate our contribution, the proposed method called E-MGRA has been applied to obtain the ranking of suitable network. Finally, a simulation has been presented to demonstrate the performances of our developed approach to reduce the number of handovers compared to the classical method.

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Smart mobility management in 5G heterogeneous networks

Cited by 29 publications

References 18 publications

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

Generalized Light Gradient Boost Classifier for Traffic Aware Seamless Mobility Management in Heterogeneous Network

Conceptual mobility model of vertical handover decision in heterogeneous networks

Improving Modified Grey Relational Method for Vertical Handover in Heterogeneous Networks

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