Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection

Li, Jiaxun; Zhao, Haitao; Wang, Haijun; Gu, Fengshou; Wei, Jibo; Yin, Hao; Ren, Baoquan

doi:10.1109/jiot.2019.2955732

Cited by 123 publications

(47 citation statements)

References 24 publications

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“…These works, however, do not consider link scheduling. The authors of [13] jointly study UAV trajectory, height, velocity and link scheduling optimization, where the UAV collects data from multiple ground users. The aim is to minimize the total mission time.…”

Section: Related Workmentioning

confidence: 99%

Joint Trajectory and Link Scheduling Optimization in UAV Networks

Zheng

Chin

2021

IEEE Access

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This paper considers data upload from multiple devices to a rotary-wing Unmanned Aerial Vehicle (UAV) equipped with a Successive Interference Cancellation (SIC) radio. The problem at hand is to optimize the UAV's data collection points, and the set of transmitting devices at each point. We outline three contributions. The first is an Integer Linear Program (ILP), which can be used to compute the optimal trajectory and data transmission schedule. Second, we propose a novel heuristic called Iteratively Construct Link Schedule and Trajectory (ICLST) that includes a link set/schedule selection policy called Highest Sum-Rate Selection (HSRS). Third, we propose a novel learning-based protocol that enables a UAV to independently learn the optimal trajectory with the highest energy efficiency. Our results show that a SIC radio helps double the amount of data collected by the UAV. Placing devices at different heights helps the UAV collect more data. Moreover, ICLST with HSRS is capable of producing a schedule that is near optimal. Additionally, our learning protocol yields a schedule with the highest energy-efficiency.

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Section: Related Workmentioning

confidence: 99%

Joint Trajectory and Link Scheduling Optimization in UAV Networks

Zheng

Chin

2021

IEEE Access

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“…An extensive study on the use of UAVs in search and rescue, construction, coverage, and transportation was conducted from a communications point of view in [5]. A significant amount of research has been done in the areas of UAV trajectory optimisation [6]- [8], collision avoidance [9]- [11], and energy efficiency [12]- [14].…”

Section: Related Workmentioning

confidence: 99%

“…The objective function in (8) aims to maximise the total user coverage of the system, which can be derived from (4)- (6). The constraint c1 makes sure the energy spent on manoeuvring the UAV-BS is within the limit of available energy.…”

Section: ) Total Coverage Of the Ground Usersmentioning

confidence: 99%

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

Abeywickrama

Dutkiewicz

et al. 2020

IEEE Open J. Veh. Technol.

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Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and costefficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. However, UAV-BSs have their limitations and complications, for instance, limited available energy. In addition, when several UAV-BSs are deployed to provide coverage to a specific area, the possibility of inter-UAV collisions and the interference to ground users increase. We propose Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) based methods to deploy UAV-BSs under energy constraints to provide efficient and fair coverage to the ground users, while minimising inter-UAV collisions and interference to ground users. The proposed methods outperform the baseline methods by an average increase of 38.94% in system fairness, 42.54% in individual user coverage, and 15.04% in total system coverage, in comparison with the baseline methods. INDEX TERMS Unmanned aerial vehicles (UAVs), wireless coverage, reinforcement learning.

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“…In order to maximize the end-toend throughput between the source node and the destination node, the multi-hop and the single-hop UAV relay systems are mainly considered [2] [6]. Besides, applying UAV as a flight access point for data collection and information dissemination has become an attractive field of research [2] [7]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…In the entire data collection process, the time of data collection is also very important. Therefore, in [12], the trajectory and the height of the UAV are directly considered to minimize the total task time.…”

Section: Introductionmentioning

confidence: 99%

Robust UAV Aided Data Collection for Energy-Efficient Wireless Sensor Network with Imperfect CSI

Fu¹,

Cen²,

Zou³

2021

JCC

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Due to its air superiority and high mobility, unmanned aerial vehicle (UAV) can obtain better line-of-sight (LoS) link transmission channel. Therefore, UAV assisted data collection for wireless sensor networks (WSNs) has become an important research direction. This paper intends to minimize the loss of WSNs for the robust data acquisition and communication assisted by UAV under the imperfect channel state information (CSI). On the premise of ensuring the completion of the communication task, we jointly optimize the wake-up schedule of SNs and the flight trajectory of the UAV, by considering the flight speed of the UAV and the sparse access of all sensor nodes (SNs) in WSN. Because the formulated optimization problem is a mixed integer nonconvex problem, we decompose the original problem into the efficient suboptimal solutions to overcome the difficulty of the optimization. Finally, the number of access node corresponding to the optimized operation time and access efficiency is induced for the entire WSN system efficiency improving. The simulation shows the performance gains of our proposed scheme and the influences of the system parameters are analyzed.

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Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection

Cited by 123 publications

References 24 publications

Joint Trajectory and Link Scheduling Optimization in UAV Networks

Joint Trajectory and Link Scheduling Optimization in UAV Networks

A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints

Robust UAV Aided Data Collection for Energy-Efficient Wireless Sensor Network with Imperfect CSI

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