Secrecy Capacity Maximization of UAV-Enabled Relaying Systems with 3D Trajectory Design and Resource Allocation

An, Qi; Pan, Yu; Han, Huizhu; Hu, Hang

doi:10.3390/s22124519

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…As a result, there is an urgent need to develop this aspect of IoUAVs to provide seamless mobility and connectivity. In [129][130][131], the authors employed a UAV that was dispatched to collect data from IoT devices under stringent time limitations. The total number of IoT devices was maximized by optimizing the UAV trajectory and wireless resource allocation simultaneously.…”

Section: Synergy Of Uavs and Iot Systemsmentioning

confidence: 99%

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

Al-Shaibani

Shayea

Çağlar

et al. 2022

Sensors

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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.

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Section: Synergy Of Uavs and Iot Systemsmentioning

confidence: 99%

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

Al-Shaibani

Shayea

Çağlar

et al. 2022

Sensors

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“…The UAV-MEC can undertake two functions of task computing offloading or relay forwarding. Task computing offloading and relay forwarding are two functions that the UAV can perform [85]. As the network's middle layer, UAV-MEC collaborates with the central cloud to process tasks with offloading and caching [86].…”

Section: Computation Offloading Utility In Uav-mecmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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“…In addition to the coverage and the throughput, there are many works aimed at other objectives for UAV-BS deployment optimization. The goal of [ 26 ] was to maximize the secrecy capacity of a UAV-enabled relaying system, which was achieved by jointly optimizing the UAV’s location, power allocation, and bandwidth allocation. This problem is formulated as a non-convex optimization problem and solved using their proposed successive convex approximation–alternative iterative optimization (SCA-AIO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on the Total Channel Capacities Pertaining to Two Coverage Layouts for Three-Dimensional, UAV-Assisted Ad Hoc Networks

Yan

Zhu

Wang

et al. 2023

Sensors

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Unmanned aerial vehicles (UAVs) employed as airborne base stations (BSs) are considered the essential components in future sixth-generation wireless networks due to their mobility and line-of-sight communication links. For a UAV-assisted ad hoc network, its total channel capacity is greatly influenced by the deployment of UAV-BSs and the corresponding coverage layouts, where square and hexagonal cells are partitioned to divide the zones individual UAVs should serve. In this paper, the total channel capacities of these two kinds of coverage layouts are evaluated using our proposed novel computationally efficient channel capacity estimation scheme. The mean distance (MD) between a UAV-BS in the network and its served users as well as the MD from these users to the neighboring UAV-BSs are incorporated into the estimation of the achievable total channel capacity. We can significantly reduce the computational complexity by using a new polygon division strategy. The simulation results demonstrate that the square cell coverage layout can always lead to a superior channel capacity (with an average increase of 7.67% to be precise) to the hexagonal cell coverage layout for UAV-assisted ad hoc networks.

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Secrecy Capacity Maximization of UAV-Enabled Relaying Systems with 3D Trajectory Design and Resource Allocation

Cited by 5 publications

References 30 publications

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

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

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Research on the Total Channel Capacities Pertaining to Two Coverage Layouts for Three-Dimensional, UAV-Assisted Ad Hoc Networks

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