A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

Ruan, Lang; Chen, Jin; Guo, Qiuju; Jiang, Han; Zhang, Yuli; Liu, Dianxiong

doi:10.3390/app8122427

Cited by 23 publications

(22 citation statements)

References 33 publications

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“…Ref. [27] regarded the UAVs as flying base stations and took the model of a coalition formation game to deal with the trade-off between coverage and transmission performance. Although in the coalition game the multi-players’ benefits are considered comprehensively, in practice, the formation of the coalition is often accompanied by losses, and it is difficult to always form a big coalition.…”

Section: Related Workmentioning

confidence: 99%

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

Xie

Tian

2019

Sensors

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Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.

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Section: Related Workmentioning

confidence: 99%

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

Xie

Tian

2019

Sensors

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“…This problem can be called the single-task, multi-robot (ST-MR) problem according to the taxonomy criteria proposed by Gerkey et al [13]. The ST-MR problem is also known as the coalition formation problem [14], a coalition means a team of UAVs performing the same target.…”

Section: Of 19mentioning

confidence: 99%

A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

et al. 2019

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A hierarchical mission planning method was proposed to solve a simultaneous attack mission planning problem for multi-unmanned aerial vehicles (UAVs). The method consisted of three phases aiming to decouple and solve the mission planning problem. In the first phase, the Pythagorean hodograph (PH) curve was used in the path estimation process for each UAV, which also served as the input for the task allocation process. In the second phase, a task allocation algorithm based on a negotiation mechanism was proposed to assign the targets. Considering the resource requirement, time-dependent value of targets and resource consumption of UAVs, the proposed task allocation algorithm can generate a feasible allocation strategy and get the maximum system utility. In the last phase, a path planning method was proposed to generate a simultaneous arrival PH path for each UAV considering UAV’s kinematic constraint and collision avoidance with obstacles. The comparison simulations showed that the path estimation process using the PH curve and the proposed task allocation algorithm improved the system utility, and the hierarchical mission planning method has potential in a real mission.

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“…Optimizing the trajectory of the UAVs is also examined in [6], which has considered throughput and energy consumption in UAV wireless networks. In [7], the authors have proposed a UAV-assisted mobile network and have analyzed an efficient trade-off between data collection and energy harvesting to ground devices. The author proposed the harvest-then-transmit protocol in UAV-enabled mobile networks [8].…”

Section: Introductionmentioning

confidence: 99%

Spectrum and Energy Efficiency in Dynamic UAV-Powered Millimeter Wave Networks

et al. 2020

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This letter investigates a dynamic scheme to analyze and optimize unmanned-aerial-vehicle-(UAV)-to-ground millimeter-wave (MMW) networks. First, a downlink energy transfer is proposed, followed by the uplink information transfer process between the UAV base stations (BSs) and a ground Internet of things (IoT) network. The UAVs fly above the ground IoT in the energy transfer phase with the dynamic regulation of the velocity according to the association distance with the IoT devices in order to minimize the path loss fading. Afterwards, the ground-based IoT devices use the received energy to transmit the uplink information by vertical links, where all the UAV BSs are modeled with gain from multiple three-dimensional uniform linear antenna arrays. After deriving new statistical properties, we analyze the spectrum efficiency and energy efficiency for the system. Our numerical results reveal that the dynamic scheme in UAV-to-ground IoT networks provides great advantages than a traditional static deployment.

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A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

Cited by 23 publications

References 33 publications

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

Spectrum and Energy Efficiency in Dynamic UAV-Powered Millimeter Wave Networks

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