Joint Trajectory and Scheduling Optimization for The Mobile UAV Aerial Base Station: A Fairness Version

Chen, Yancheng; Li, Ning; Zhong, Xijian; Xie, Wei

doi:10.3390/app9153101

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Since UAV cannot completely cover all the users in a time slot, it is necessary to optimize the user scheduling to ensure a secure communication for each user. According to [27] and [35], constraint 1…”

Section: Numerical Results and Analysismentioning

confidence: 99%

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Joint Trajectory Design and User Scheduling for Secure Aerial Underlay IoT Systems

Lei

Haosi

Park

et al. 2023

IEEE Internet Things J.

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Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are line-of-sight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make them vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. In this work, we investigate the security of aerial underlay Internet of Things (IoT) systems by jointly designing trajectory and user scheduling. An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial jammer transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the secondary user is maximized by optimizing multiple users' scheduling, the UAVs' trajectory, and transmit power. To solve the non-convex optimization problem with mixed multi-integer variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial in improving the secrecy performance of aerial underlay IoT systems.

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Section: Numerical Results and Analysismentioning

confidence: 99%

“…To enable all the CUs to be served fairly during flight time, a binary variable θ k (n) is utilized to characterize user scheduling in the considered CRN [35]. More specifically, θ k (n) = 1 signifies that only D k is scheduled to communicate with S at the nth slot.…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

Joint Trajectory Design and User Scheduling for Secure Aerial Underlay IoT Systems

Lei

Haosi

Park

et al. 2023

IEEE Internet Things J.

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show abstract

“…This parametric equation is used to calculate the UAV's X and Y location points on the rotated ellipsoidal path, using parameter δ. Specifically, for any given time t, δ(t) = 2π T (t) calculates the UAV's position along the ellipsoidal path [42], where 2π and T represent the complete circumference of the ellipse and the total time for the UAV to complete one cycle over the ellipse, respectively.…”

Section: Equations Of the Uavs Ellipsoidal Movementmentioning

confidence: 99%

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Sadia,

Akter,

Yoon

2023

Applied Sciences

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Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.

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“…To enable all the CUs to be served fairly in one period, a binary variable θ k (n) is utilized to characterize user scheduling in the considered CRN [25]. More specifically, θ k (n) = 1 signifies that only D k is scheduled to communicate with S at the nth slot.…”

Section: A System Modelmentioning

confidence: 99%

Joint Trajectory Design and User Scheduling of Aerial Cognitive Radio Networks

Lei¹,

Haosi²,

Park³

et al. 2022

Preprint

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Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are line-ofsight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make it vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. This paper investigates the security of aerial cognitive radio networks (CRNs). An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial base station transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the cognitive user is maximized by optimizing multiple users' scheduling, the UAVs' trajectory, and transmit power. To solve the nonconvex optimization problem with mixed multiple integers variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial to improving the secrecy performance of aerial CRNs.

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Joint Trajectory and Scheduling Optimization for The Mobile UAV Aerial Base Station: A Fairness Version

Cited by 4 publications

References 22 publications

Joint Trajectory Design and User Scheduling for Secure Aerial Underlay IoT Systems

Joint Trajectory Design and User Scheduling for Secure Aerial Underlay IoT Systems

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Joint Trajectory Design and User Scheduling of Aerial Cognitive Radio Networks

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