Relevant bibliographies by topics / UAV-aided Wireless Networks

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Academic literature on the topic 'UAV-aided Wireless Networks'

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Published: 25 May 2024

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Journal articles on the topic "UAV-aided Wireless Networks"

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Teng, Sihao. "UAV Assisted Wireless Network." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/2078/1/012022.

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Abstract With the increasing demand of social network service, the unmanned aerial vehicle has been used as a base station to assist terrestrial base station to improve wireless network performance. UAV base station provides high efficiency and wider data transmitting range due to the small size and flexibility of UAV. However, UAV wireless network faces few challenges. Energy efficiency is hard to achieve due to small battery capacity. The base station performance is also very important. It can be determined by aircraft’s flying stability, the performance of air to ground communication and the limitation of wireless coverage of UAV. In order to achieve optimal UAV deployment, improving deployment delay, communication coverage and UAV number limitation are important. Trajectory optimizing problems also need to be considered. This article analyzes UAV assisted wireless networks through investigating UAV energy efficiency, UAV aided network performance, optimal deployment methods and flight trajectory. It is shown that energy efficiency can be optimized by applying LoS based channel in UAV trajectory planning. And inequality iteration algorithm proposed by former researchers is used to determine optimal flight trajectory. This method is efficient because of cellular network’s interference-free ability. As for performance, channel selection methods are used to reduce overflow rate and boost data-received size. These methods are analyzed and proved to be effective for improving UAV aided wireless network performance.
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Arafat, Muhammad Yeasir, Md Arafat Habib, and Sangman Moh. "Routing Protocols for UAV-Aided Wireless Sensor Networks." Applied Sciences 10, no. 12 (June 12, 2020): 4077. http://dx.doi.org/10.3390/app10124077.

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Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.
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Iakovlev, Roman, and Anton Saveliev. "Approach to implementation of local navigation of mobile robotic systems in agriculture with the aid of radio modules." Telfor Journal 12, no. 2 (2020): 92–97. http://dx.doi.org/10.5937/telfor2002092i.

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In this paper an approach is presented, enabling to solve the problem of local navigation of mobile robotic platforms (MRP), based on utilization of wireless networks with mesh topology. Establishment of wireless networks was ensured, based on the set of radio modules, mounted on unmanned aerial vehicles (UAV), comprising a swarm. This paper presents a developed algorithm for establishment of such wireless networks, aided by LoRa-technology, as well as an algorithm for MRP localization, based on analysis of signal level, where the incoming signals are fed from MRP group radio modules to radio modules of wireless data transfer network. An algorithmic model is given for task distribution among UAV and to implement navigational capabilities of MRP swarm. In some experiments descending dependencies of absolute error value, pertinent to MRP, from the number of UAV in action were revealed, as well as of averaged deflection value of MRP positions in motion along their paths from the number of UAV in action. Thereby the averaged value of MRP localization error, depending on the number of UAV in action, was from 8.14 to 17.13 m, and the averaged value of MRP position deflection - from 16.38 to 57.12 m, respectively.
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Hua, Meng, Yi Wang, Zhengming Zhang, Chunguo Li, Yongming Huang, and Luxi Yang. "Power-Efficient Communication in UAV-Aided Wireless Sensor Networks." IEEE Communications Letters 22, no. 6 (June 2018): 1264–67. http://dx.doi.org/10.1109/lcomm.2018.2822700.

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Hua, Meng, Yi Wang, Zhengming Zhang, Chunguo Li, Yongming Huang, and Luxi Yang. "Energy-efficient optimisation for UAV-aided wireless sensor networks." IET Communications 13, no. 8 (May 14, 2019): 972–80. http://dx.doi.org/10.1049/iet-com.2018.5441.

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Liu, Bin, and Hongbo Zhu. "Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks." Sensors 19, no. 11 (May 31, 2019): 2506. http://dx.doi.org/10.3390/s19112506.

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Unmanned aerial vehicles (UAVs) are capable of serving as a data collector for wireless sensor networks (WSNs). In this paper, we investigate an energy-effective data gathering approach in UAV-aided WSNs, where each sensor node (SN) dynamically chooses the transmission modes, i.e., (1) waiting, (2) conventional sink node transmission, (3) uploading to UAV, to transmit sensory data within a given time. By jointly considering the SN’s transmission policy and UAV trajectory optimization, we aim to minimize the transmission energy consumption of the SNs and ensure all sensory data completed collected within the given time. We take a two-step iterative approach and decouple the SN’s transmission design and UAV trajectory optimization process. First, we design the optimal SNs transmission mode policy with preplanned UAV trajectory. A dynamic programming (DP) algorithm is proposed to obtain the optimal transmission policy. Then, with the fixed transmission policy, we optimize the UAV’s trajectory from the preplanned trace with recursive random search (RRS) algorithm. Numerical results show that the proposed scheme achieves significant energy savings gain over the benchmark schemes.
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Berrahal, Sarra, Jong-Hoon Kim, Slim Rekhis, Noureddine Boudriga, Deon Wilkins, and Jaime Acevedo. "Border surveillance monitoring using Quadcopter UAV-Aided Wireless Sensor Networks." Journal of Communications Software and Systems 12, no. 1 (March 22, 2016): 67. http://dx.doi.org/10.24138/jcomss.v12i1.92.

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In this paper we propose a novel cooperative bordersurveillance solution, composed of a Wireless Sensor Network (WSN) deployed terrestrially to detect and track trespassers, and a set of lightweight unmanned aircraft vehicles (UAVs) in the form of quadcopters that interact with the deployed WSN to improve the border surveillance, the detection and investigation of network failures, the maintenance of the sensor network, the tracking of trespasser, the capture and transmission of realtime video of the intrusion scene, and the response to hostage situations. A heuristic-based scheduling algorithm is described to optimize the tracking mission by increasing the rate of detected trespassers spotted by the quadcopters. Together with the design of the electrical, mechanical and software architecture of the proposed VTail quadcopter, we develop in this paper powerless techniques to accurately localize terrestrial sensors using RFID technology, compute the optimal positions of the new sensors to drop, relay data between isolated islands of nodes, and wake up sensors to track intruders. The developed VTail prototype is tested to provide valid and accurate parameters’ values to the simulation. The latter is conducted to evaluate the performance of the proposed WSN-based surveillance solution.
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Shi, Baihua, Yang Wang, Danqi Li, Wenlong Cai, Jinyong Lin, Shuo Zhang, Weiping Shi, Shihao Yan , and Feng Shu. "STAR-RIS-UAV-Aided Coordinated Multipoint Cellular System for Multi-User Networks." Drones 7, no. 6 (June 17, 2023): 403. http://dx.doi.org/10.3390/drones7060403.

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Different from conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit signals to the receiver. In this paper, to serve more ground users and increase deployment flexibility, we investigate an unmanned aerial vehicle (UAV) equipped with STAR-RIS (STAR-RIS-UAV)-aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV-aided coordinated multipoint (CoMP) cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficient matrices are optimized. Specifically, instead of adopting the alternating optimization, we design an iteration method to optimize all variables for both the ES and MS protocols at the same time. Simulation results reveal that the STAR-RIS-UAV-aided CoMP system has a much higher sum rate than systems with conventional RIS or without RIS. Furthermore, the proposed structure is more flexible than fixed STAR-RIS and could greatly promote the sum rate.
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Cao, Dongju, Wendong Yang, and Gangyi Xu. "Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks." Applied Sciences 9, no. 24 (December 15, 2019): 5524. http://dx.doi.org/10.3390/app9245524.

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With the rapid development and evolvement of unmanned aerial vehicle (UAV) technology, UAV aided wireless communication technology has been widely studied recently. In this paper, a buffer aided multi-UAV relaying network is investigated to assist blocked ground communication. According to the mobility and implementation flexibility of UAV relays, it is assumed that the communication link between air-to-ground is the Rician fading channel. On the basis of information causality, we derive the state change of the information in the buffer of UAV relays and maximize the end-to-end average throughput by join the relay selection, UAV transmit power, and UAV trajectory optimization. However, the considered problem is a mixed integer non-convex optimization problem, and therefore, it is difficult to solve directly with general optimization methods. In order to make the problem tractable, an efficient iterative algorithm based on the block coordinate descent and the successive convex optimization techniques is proposed. The convergence of the proposed algorithm will be verified analytically at the end of this paper. The simulation results show that by alternately optimizing the relay selection, UAV transmit power, and UAV trajectory, the proposed algorithm is able to achieve convergence quickly and significantly improve the average throughput, as compared to other benchmark schemes.
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Castellanos, German, Margot Deruyck, Luc Martens, and Wout Joseph. "Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks." Sensors 19, no. 15 (July 30, 2019): 3342. http://dx.doi.org/10.3390/s19153342.

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Today’s wireless networks provide us reliable connectivity. However, if a disaster occurs, the whole network could be out of service and people cannot communicate. Using a fast deployable temporally network by mounting small cell base stations on unmanned aerial vehicles (UAVs) could solve the problem. Yet, this raises several challenges. We propose a capacity-deployment tool to design the backhaul network for UAV-aided networks and to evaluate the performance of the backhaul network in a realistic scenario in the city center of Ghent, Belgium. This tool assigns simultaneously resources to the ground users—access network—and to the backhaul network, taking into consideration backhaul capacity and power restrictions. We compare three types of backhaul scenarios using a 3.5 GHz link, 3.5 GHz with carrier aggregation (CA) and the 60 GHz band, considering three different types of drones. The results showed that an optimal UAV flight height (80 m) could satisfy both access and backhaul networks; however, full coverage was difficult to achieve. Finally, we discuss the influence of the flight height and the number of requesting users concerning the network performance and propose an optimal configuration and new mechanisms to improve the network capacity, based on realistic restrictions.
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Dissertations / Theses on the topic "UAV-aided Wireless Networks"

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Bayerlein, Harald. "Machine Learning Methods for UAV-aided Wireless Networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS154.

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Les drones autonomes sont envisagés pour une multitude d'applications au service de la société du futur. Du point de vue des réseaux sans-fil de la prochaine génération, les drones ne sont pas seulement prévus dans le rôle d'utilisateurs passifs connectés au réseau cellulaire, mais aussi comme facilitateurs actifs de la connectivité dans le cadre de réseaux assistés par drones. L'avantage déterminant des drones dans tous les scénarios d'application potentiels est leur mobilité. Pour tirer pleinement parti de leurs capacités, des méthodes de planification de trajectoire flexibles et efficaces sont une nécessité impérative. Cette thèse se concentre sur l'exploration de l'apprentissage automatique, en particulier l'apprentissage par renforcement (RL), comme une classe prometteuse de solutions aux défis de la gestion de la mobilité des drones. L'apprentissage par renforcement profond est l'un des rares cadres qui nous permet de nous attaquer directement à la tâche complexe du contrôle des drones dans les scénarios de communication, étant donné qu'il s'agit généralement de problèmes d'optimisation non convexes et NP-difficile. De plus, le RL profond offre la possibilité d'équilibrer les objectifs multiples de manière directe, il est très flexible en termes de disponibilité d'informations préalables ou de modèles, tandis que l'inférence RL profonde est efficace sur le plan informatique. Cette thèse explore également les défis que représentent un temps de vol fortement limité, la coopération entre plusieurs drones et la réduction de la demande de données d'entraînement. La thèse explore aussi la connexion entre les réseaux assistés par drone et la robotique
Autonomous unmanned aerial vehicles (UAVs), spurred by rapid innovation in drone hardware and regulatory frameworks during the last decade, are envisioned for a multitude of applications in service of the society of the future. From the perspective of next-generation wireless networks, UAVs are not only anticipated in the role of passive cellular-connected users, but also as active enablers of connectivity as part of UAV-aided networks. The defining advantage of UAVs in all potential application scenarios is their mobility. To take full advantage of their capabilities, flexible and efficient path planning methods are necessary. This thesis focuses on exploring machine learning (ML), specifically reinforcement learning (RL), as a promising class of solutions to UAV mobility management challenges. Deep RL is one of the few frameworks that allows us to tackle the complex task of UAV control and deployment in communication scenarios directly, given that these are generally NP-hard optimization problems and badly affected by non-convexity. Furthermore, deep RL offers the possibility to balance multiple objectives of UAV-aided networks in a straightforward way, it is very flexible in terms of the availability of prior or model information, while deep RL inference is computationally efficient. This thesis also explores the challenges of severely limited flying time, cooperation between multiple UAVs, and reducing the training data demand of DRL methods. The thesis also explores the connection between drone-assisted networks and robotics, two generally disjoint research communities
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Book chapters on the topic "UAV-aided Wireless Networks"

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Shi, Weisen, Junling Li, and Ning Zhang. "Resource Allocation in UAV-Aided Wireless Networks." In Encyclopedia of Wireless Networks, 1222–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_345.

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Shi, Weisen, Junling Li, and Ning Zhang. "Resource Allocation in UAV-Aided Wireless Networks." In Encyclopedia of Wireless Networks, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_345-1.

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"UAV-Aided Wireless Network." In Encyclopedia of Wireless Networks, 1423. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_300676.

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Conference papers on the topic "UAV-aided Wireless Networks"

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Esrafilian, Omid, Rajeev Gangula, and David Gesbert. "Autonomous UAV-aided Mesh Wireless Networks." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2020. http://dx.doi.org/10.1109/infocomwkshps50562.2020.9162753.

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Ma, Xiaoyan, Tianyi Liu, Rahim Kacimi, Riadh Dhaou, and Song Liu. "Duration-aware Data Collection in UAV-aided Mobile Sensor Networks." In 2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021. http://dx.doi.org/10.1109/iwcmc51323.2021.9498971.

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Yang, Longan, Zhiyuan Su, Haibing Yang, Zhixiong Na, and Feng Yan. "An Efficient Charging Algorithm for UAV-aided Wireless Sensor Networks." In 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc51575.2020.9345142.

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Xue, Sheng, Suzhi Bi, and Xiaohui Lin. "Energy Minimization in UAV-Aided Wireless Sensor Networks with OFDMA." In 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2019. http://dx.doi.org/10.1109/wcsp.2019.8927916.

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Marini, Riccardo, Sangwoo Park, Osvaldo Simeone, and Chiara Buratti. "Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks." In ICC 2023 - IEEE International Conference on Communications. IEEE, 2023. http://dx.doi.org/10.1109/icc45041.2023.10279524.

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Lakiotakis, Emmanouil, Nikolaos Pappas, and Xenofontas Dimitropoulos. "Modeling the Age of Information in UAV-aided Wireless Networks." In 2022 IEEE Conference on Standards for Communications and Networking (CSCN). IEEE, 2022. http://dx.doi.org/10.1109/cscn57023.2022.10051021.

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Pang, Xiaowei, Zan Li, Xiaoming Chen, Yang Cao, Nan Zhao, Yunfei Chen, and Zhiguo Ding. "UAV-Aided NOMA Networks with Optimization of Trajectory and Precoding." In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018. http://dx.doi.org/10.1109/wcsp.2018.8555640.

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Sharma, Vatsala, Prajwalita Saikia, Sandeep Kumar Singh, Keshav Singh, Wan-Jen Huang, and Sudip Biswas. "FEEL-enhanced Edge Computing in Energy Constrained UAV-aided IoT Networks." In 2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023. http://dx.doi.org/10.1109/wcnc55385.2023.10118939.

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Mao, Shenshen, Feng Yan, Jiahui Chen, Fei Shen, Weiwei Xia, Jin Hu, and Lianfeng Shen. "An Energy Efficient Charging Scheme for UAV-aided Wireless Sensor Networks." In 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019. http://dx.doi.org/10.1109/iccc47050.2019.9064340.

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Chen, Jiahui, Feng Yan, Shenshen Mao, Fei Shen, Weiwei Xia, Yi Wu, and Lianfeng Shen. "Efficient Data Collection in Large-Scale UAV-aided Wireless Sensor Networks." In 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2019. http://dx.doi.org/10.1109/wcsp.2019.8927929.

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