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Applied Sciences
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Vertical Handover Management in Heterogeneous Wireless Networks
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Vertical Handover Management in Heterogeneous Wireless Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 10947

Special Issue Editor

Dr. Murad Khan

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
Interests: vertical handover management; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vertical handover management (VHM) provides uninterrupted connection services in heterogeneous wireless networks (HWN). However, providing uninterrupted services in real-time networks such as in fast-moving vehicles scenarios and locations where fast connectivity cannot be provided is a challenging job. In this regard, IEEE proposed a new standard called the IEEE Media Independent Handover Standard 802.21 (IEEE 802.21 MIH) in 2008 to provide seamless and uninterrupted services in HWN. However, there are a number of challenges in the IEEE 802.21 MIH standard, such as considering received signal strength (RSS) for triggering handover and network selection, affected by the too-early and too-late handover, interoperability among heterogeneous networks, etc. Similarly, 5G and beyond 5G technologies aim to incorporate machine and deep learning techniques for fast handover management, location management, and data analytics related to enabling user-centric handover management. Therefore, in this Special Issue, we aim to give a deep insight into the latest research going on in the domain of VHM based on intelligent networking with theoretical and experimental analysis. Finally, research studies and findings related to the following points are welcome to the SI:

  • Issues and challenges in enabling handover in 5G and beyond 5G technologies;
  • User-centric handover management;
  • Machine and deep learning techniques in handover management;
  • Software-defined networking based-handover management;
  • Data analytics related to handover management;
  • Intelligent networking and mobility services;
  • Handover over Internet technologies;
  • Fast and seamless handover management;
  • Geographical location management for fast handover services;
  • Cell and cell-less vertical handover;
  • Techniques to enable handover in vehicular networks;
  • Management of vertical handover in IoT networks;
  • Visualizing the handover traffic in a dense network scenario.

Dr. Murad Khan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Issues and challenges in enabling handover in 5G and beyond 5G technologies
  • User-centric handover management
  • Machine and deep learning techniques in handover management
  • Software-defined networking based-handover management
  • Data analytics related to handover management
  • Intelligent networking and mobility services
  • Handover over Internet technologies
  • Fast and seamless handover management
  • Geographical location management for fast handover services
  • Cell and cell-less vertical handover
  • Techniques to enable handover in vehicular networks
  • Management of vertical handover in IoT-networks
  • Visualizing the handover traffic in a dense network scenario.

Published Papers (2 papers)

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Research

25 pages, 596 KiB  
Article
An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks
by Jawad Tanveer, Amir Haider, Rashid Ali and Ajung Kim
Appl. Sci. 2022, 12(1), 426; https://doi.org/10.3390/app12010426 - 03 Jan 2022
Cited by 39 | Viewed by 7701
Abstract
The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high [...] Read more.
The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed. Full article
(This article belongs to the Special Issue Vertical Handover Management in Heterogeneous Wireless Networks)
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24 pages, 5950 KiB  
Article
A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics
by Mykola Beshley, Natalia Kryvinska, Oleg Yaremko and Halyna Beshley
Appl. Sci. 2021, 11(11), 4737; https://doi.org/10.3390/app11114737 - 21 May 2021
Cited by 15 | Viewed by 2483
Abstract
With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the [...] Read more.
With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a wireless access node in a heterogeneous environment. A structural diagram of the optimization stages for wireless heterogeneous networks was developed, making it possible to improve the efficiency of their functioning. A model for studying the processes of functioning of a heterogeneous network environment is proposed. This model uses the methodology of big data evaluation to perform data transmission monitoring, analysis of tasks generated by network users, and statistical output of vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of the heterogeneous network is increased by 16% when using the method of static reservation of network resources, compared to homogeneous networks, and another 13% when using a uniform distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users. Full article
(This article belongs to the Special Issue Vertical Handover Management in Heterogeneous Wireless Networks)
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