Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

W. T. Alshaibani*, Ibraheem Shayea*, Ramazan Caglar, Jafri Din, Yousef Ibrahim Daradkeh

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

23 Citations (Scopus)

Abstract

The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

Original languageEnglish
Article number6013
JournalSensors
Volume22
Issue number16
DOIs
Publication statusPublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Funding

This research has been produced benefiting from the 2232 International Fellowship for Out-standing Researchers Program of TÜBİTAK (Project No: 118C276) conducted at Istanbul Technical University (İTÜ), and it was also supported in part by Universiti Teknologi Malaysia (UTM), Malaysia.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu118C276
Universiti Teknologi Malaysia

    Keywords

    • 5G networks
    • UAV
    • connected drones
    • deep learning
    • drones
    • handover
    • heterogeneous 6G networks
    • machine learning
    • mobility management

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