Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Huang, Yeting; Qi, Nan; Jia, Luliang; Wu, Qihui; Yao, Rugui; Wang, Wenjing

doi:10.1109/ojcoms.2022.3232600

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…On the premise of satisfying the communication service quality of all ground users to be rescued within the coverage range of UAV base stations, the efficiency of UAV base station power consumption can be improved by enabling the UAV base stations to cover the rescue ground users with a minimum transmission power. The minimum launch power of the UAV base stations is expressed as: P t = P r + P loss − P extra (20) where P extra is a power loss function that varies according to different environments.…”

Section: Minimum Power Consumption Simulation Of Uavs Base Stationmentioning

confidence: 99%

“…In [19], the authors presented a combined approach using the steepest descent method and genetic algorithm to optimize the deployment scheme of wireless sensor nodes, aiming to achieve network connectivity while meeting coverage constraints. In [20], the authors proposed a graphic coalition formation game that combines UAV time-varying topology with a coalition formation game.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

Gao,

Wang

2023

Applied Sciences

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The collaborative deployment of multiple UAVs is a crucial issue in UAV-supported disaster emergency communication networks, as utilizing these UAVs as air base stations can greatly assist in restoring communication networks within disaster-stricken areas. In this paper, the problem of rapid deployment of randomly distributed UAVs in disaster scenarios is studied, and a distributed rapid deployment method for UAVs´ emergency communication network is proposed; this method can cover all target deployment points while maintaining connectivity and provide maximum area coverage for the emergency communication network. To reduce the deployment complexity, we decoupled the three-dimensional UAV deployment problem into two dimensions: vertical and horizontal. For this small-area deployment scenario, a small area UAVs deployment improved-Broyden–Fletcher–Goldfarb–Shanno (SAIBFGS) algorithm is proposed via improving the Iterative step size and search direction to solve the high computational complexity of the traditional Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In a large area deployment scenario, aiming at the problem of the premature convergence of the standard genetic algorithm (SGA), the large-area UAVs deployment elitist strategy genetic algorithm (LAESGA) is proposed through the improvement of selection, crossover, and mutation operations. The adaptation function of connectivity and coverage is solved by using SAIBFGS and LAESGA, respectively, in the horizontal dimension to obtain the optimal UAV two-dimensional deployment coordinates. Then, the transmitting power and height of the UAV base station are dynamically adjusted according to the channel characteristics and the discrete coefficients of the ground users to be rescued in different environments, which effectively improves the power consumption efficiency of the UAV base station and increases the usage time of the UAV base station, realizing the energy-saving deployment of the UAV base station. Finally, the effectiveness of the proposed method is verified via data transmission rate simulation results in different environments.

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Section: Minimum Power Consumption Simulation Of Uavs Base Stationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

Gao,

Wang

2023

Applied Sciences

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“…An UAV formation control law was proposed to generate a leader-follower structure based on consistency under the balance of control constraints and communication constraints, so as to avoid collisions and maintain connectivity between UAVs [7]. The authors in [8] proposed using the graph coalition formation game to model the cooperation between UAVs, which can quickly restore the required connectivity between UAV networks. In [9], the connectivity methods were compared in four application scenarios, mainly by increasing or decreasing the communication links between UAVs to increase or decrease the connectivity of UAV clusters.…”

Section: Introductionmentioning

confidence: 99%

Connectivity-Maintenance UAV Formation Control in Complex Environment

Zhu

et al. 2023

Drones

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Cooperative formation control is the research basis for various tasks in the multi-UAV network. However, in a complex environment with different interference sources and obstacles, it is difficult for multiple UAVs to maintain their connectivity while avoiding obstacles. In this paper, a Connectivity-Maintenance UAV Formation Control (CMUFC) algorithm is proposed to help multi-UAV networks maintain their communication connectivity by changing the formation topology adaptively under interference and reconstructing the broken communication topology of a multi-UAV network. Furthermore, through the speed-based artificial potential field (SAPF), this algorithm helps the multi-UAV formation to avoid various obstacles. Simulation results verify that the CMUFC algorithm is capable of forming, maintaining, and reconstructing multi-UAV formation in complex environments.

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“…In addition, the huge amount of data generated by the devices of the IoT can be used to extract useful information through machine learning, thus achieving a variety of intelligent IoT services. Emerging applications of the intelligent IoT include automobile driving [7]- [8], unmanned aerial vehicles (UAV) [9]- [10], robots [11], health care [12], supply chain finance, and so on. However, in the 6G era, the paradigm shift from 'interconnected things' to 'interconnected intelligence' through modern machine learning technology is facing three major challenges.…”

Section: Introductionmentioning

confidence: 99%

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

Hong

Al‐Otaibi

et al. 2023

IEEE Open J. Commun. Soc.

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Currently, with the widespread of the intelligent internet of things (IoT) in beyond 5G, wireless federated learning (WFL) has attracted a lot of attention to enable knowledge construction and sharing among a huge amount of distributed edge devices. However, under unstable wireless channel conditions, existing WFL schemes exist the following challenges: First, learning model parameters will be disturbed by bit errors because of interference and noise during wireless transmission, which will affect the training accuracy and the loss of the learning model. Second, traditional edge devices with CPU acceleration are inefficient due to the low throughout computation, especially in accelerating the encoding and decoding process during wireless transmission. Third, current hardware-level GPU acceleration methods cannot optimize complex operations, for instance, complex wireless coding in the WFL environment. To address the above challenges, we propose a software-defined GPU-CPU empowered efficient WFL architecture with embedding LDPC communication coding. Specifically, we embed wireless channel coding into the server weight aggregation and the client local training process respectively to resist interruptions in the learning process and design a GPU-CPU acceleration scheme for this architecture. The experimental results show its anti-interference ability and GPU-CPU acceleration ability during wireless transmission, which is 10 times the error control capability and 100 times faster than existing WFL schemes.

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Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Cited by 5 publications

References 45 publications

Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

Connectivity-Maintenance UAV Formation Control in Complex Environment

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

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