3D Deployment of Multi-UAV for Energy-Saving: A Game-Based Learning Approach

Yu, Xingyue; Yao, Junnan; Ruan, Lang; Yao, Kailing; Liu, Dianxiong; Chen, Runfeng

doi:10.1109/iccc47050.2019.9064450

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…A joint optimization of the UAV deployment and power allocation was performed in [246] to maximize the area of service coverage and enhance EE. The optimization problem was decomposed into two separate problems involving the determination of the optimal transmit power control and 3D location.…”

Section: Conventional Approachesmentioning

confidence: 99%

“…Year Category Specific Algorithm CA ML [237] 2018 Heuristic [238] 2021 Heuristic [239] 2021 Heuristic [240] 2021 Heuristic [241] 2020 Heuristic [242] 2021 Heuristic [243] 2019 Heuristic [244] 2020 Heuristic [245] 2019 Heuristic [246] 2019 Heuristic [247] 2020 Heuristic [27] 2019 Heuristic [242] 2021 Heuristic [248] 2020 Heuristic Here, we consider studies that involve the scheduling UAV-BSs flights as well as their sequence of transmission in order to ensure that minimum energy is consumed in the process. We also examine research works that investigate how the fight of UAVs are coordinated and scheduled or how UAVs cooperate to achieve energy-efficient communication.…”

Section: Papermentioning

confidence: 99%

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A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Conventional Approachesmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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“…The UAV‐aided WSN needs to comprehensively consider such issues as task scheduling, link performance analysis, path planning, resource allocation, flight time, cell association, energy efficiency, and return link [21]. This is a highly interdisciplinary problem that requires not only the use of traditional field tools such as communication theory, optimization theory, and network design but also critical technologies in emerging fields such as stochastic geometry, machine learning, and game theory.…”

Section: Introductionmentioning

confidence: 99%

“…Proof. The minimum amount of data collected constantly must be higher than Q min k in constraint (21), which is a positive number, in order for this constraint to be true there must be some 𝜅 k [n] equal to one. This enables constraint (21) to be equivalent to constraint (12).…”

Section: Theorem 2 By Introducing the Auxiliary Variable 𝜅 K [N] ∀N K...mentioning

confidence: 99%

“…The minimum amount of data collected constantly must be higher than Q min k in constraint (21), which is a positive number, in order for this constraint to be true there must be some 𝜅 k [n] equal to one. This enables constraint (21) to be equivalent to constraint (12). 𝜅 k [n] can only be one within the maximum communication range d max k in constraint (20) equivalent constraint (13), otherwise, it would be 0.…”

Section: Theorem 2 By Introducing the Auxiliary Variable 𝜅 K [N] ∀N K...mentioning

confidence: 99%

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Completion time minimization for UAV enabled data collection with communication link constrained

Guo

Zhang

et al. 2022

IET Communications

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This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low‐power ground sensor nodes (SNs) in multi‐obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non‐convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem‐like (TSP‐like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non‐convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

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Coverage Maximization by Optimal Positioning and Transmission Planning for UAV-Assisted Wireless Communications

Saym

Mahbub

Ahmed

2021

2021 International Conference on Science &Amp; Contemporary Technologies (ICSCT)

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3D Deployment of Multi-UAV for Energy-Saving: A Game-Based Learning Approach

Cited by 8 publications

References 23 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Completion time minimization for UAV enabled data collection with communication link constrained

Coverage Maximization by Optimal Positioning and Transmission Planning for UAV-Assisted Wireless Communications

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