Multistage Clustering-Based Localization for Remote UAV Swarm: A Coalitional Game Framework

Ruan, Lang; Li, Guangxia; Cheng, Jian; Lv, Jing; Dai, Weiheng; Tian, Shengfeng; Hu, Jing

doi:10.1109/lcomm.2022.3184714

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…To solve this problem, network clustering has become a feasible method. However, with the advancement of hardware technology, the number of sensors in AUSCNs has increased rapidly, resulting in the complexity of the network topology [13][14][15]. It is difficult for traditional clustering algorithms to find appropriate head nodes to reduce energy consumption [16].…”

Section: Introductionmentioning

confidence: 99%

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Zhong,

Zhou,

Zhang

2023

Applied Sciences

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Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

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Section: Introductionmentioning

confidence: 99%

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Zhong,

Zhou,

Zhang

2023

Applied Sciences

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“…The clustering head of a UAV swarm has been discussed by [11], wherein effective communication management has been addressed. Additionally, a multistage cluster of remotely located UAV swarm has also been addressed by Rang Ruan et al [12]. The UAV swarm coordination protocol for supporting the assigned mission was reported by [13].…”

Section: Introductionmentioning

confidence: 99%

A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

Khalil

Rahman

Ullah

et al. 2022

Drones

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This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances.

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“…They analyzed a dataset comprising five triangular swarm formations using the K-means clustering method and studied dendrograms to identify correct swarm formations. In [24], a UAV swarm model was developed with a multilevel-based clustering approach. The authors proposed a tree-based multi-stage scheme employing clustering localization and designed a localization algorithm using a coalitional game framework to explore the model's features.…”

mentioning

confidence: 99%

Efficient Sensing Data Collection with Diverse Age of Information in UAV-Assisted System

Pei,

Hou,

Zhang

et al. 2023

IoT

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With the high flexibility and low cost of the deployment of UAVs, the application of UAV-assisted data collection has become widespread in the Internet of Things (IoT) systems. Meanwhile, the age of information (AoI) has been adopted as a key metric to evaluate the quality of the collected data. Most of the literature generally focuses on minimizing the age of all information. However, minimizing the overall AoI may lead to high costs and massive energy consumption. In addition, not all types of data need to be updated highly frequently. In this paper, we consider both the diversity of different tasks in terms of the data update period and the AoI of the collected sensing information. An efficient data collection method is proposed to maximize the system utility while ensuring the freshness of the collected information relative to their respective update periods. This problem is NP-hard. With the decomposition, we optimize the upload strategy of sensor nodes at each time slot, as well as the hovering positions and flight speeds of UAVs. Simulation results show that our method ensures the relative freshness of all information and reduces the time-averaged AoI by 96.5%, 44%, 90.4%, and 26% when the number of UAVs is 1 compared to the corresponding EMA, AOA, DROA, and DRL-eFresh, respectively.

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Multistage Clustering-Based Localization for Remote UAV Swarm: A Coalitional Game Framework

Cited by 4 publications

References 12 publications

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

Efficient Sensing Data Collection with Diverse Age of Information in UAV-Assisted System

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