Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Rolly, Rinju Mariam; Malarvezhi, P.; Λάγκας, Θωμάς

doi:10.1177/15501329221123933

Cited by 18 publications

(5 citation statements)

References 119 publications

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“…The article [20] offers a comprehensive survey of the various applications and algorithms that pertain to using UAVs as aerial BSs. The purpose of article is to review each research area in detail, covering the major technology and applications oriented around the development of UAVs serving as base stations.…”

Section: Related Workmentioning

confidence: 99%

Availability of Services in Wireless Sensor Network with Aerial Base Station Placement

Kabashkin

2023

JSAN

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Internet of Things technologies use many sensors combined with wireless networks for cyber-physical systems in various applications. Mobility is an essential characteristic for many objects that use sensors. In mobile sensor networks, the availability of communication channels is crucial, especially for mission-critical applications. This article presents models for analyzing the availability of sensor services in a wireless network with aerial base station placement (ABSP), considering the real conditions for using unmanned aerial vehicles (UAVs). The studied system uses a UAV-assisted mobile edge computing architecture, including ABSP and a ground station for restoring the energy capacity of the UAVs, to maintain the availability of interaction with the sensors. The architecture includes a fleet of additional replacement UAVs to ensure continuous communication coverage for the sensor network during the charging period of the air-based station UAVs. Analytical expressions were obtained to determine the availability of sensor services in the system studied.

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Section: Related Workmentioning

confidence: 99%

Availability of Services in Wireless Sensor Network with Aerial Base Station Placement

Kabashkin

2023

JSAN

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“…Extensive research is being conducted on many aspects of such networks, including their deployment, performance analysis, resource management, path optimization, and channel modeling. With a comprehensive review of all relevant study areas, Rolly RM (2022) concentrates on the various applications and related algorithms for realizing aerial base stations [16]. In summary, this article discusses the technology, important applications, and challenges involved in designing and analyzing UAVs for use as base stations.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Srinivas,

Venkatramulu,

Chandra Shekar Rao

et al. 2023

IJRITCC

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Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity. Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.

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“…Lightweight, compact, and inexpensive drones are widely used and applied in many fields such as agricultural production, disaster relief, safety prevention and control, and express delivery. They have brought tremendous impacts to people's production and lives [1]. With the continuous expansion of drone applications, the demand for drone object detection is also increasing.…”

Section: Introductionmentioning

confidence: 99%

A Small Object Detection Method for Drone-Captured Images Based on Improved YOLOv7

Zhao,

Shao,

Liu

et al. 2024

Remote Sensing

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Due to the broad usage and widespread popularity of drones, the demand for a more accurate object detection algorithm for images captured by drone platforms has become increasingly urgent. This article addresses this issue by first analyzing the unique characteristics of datasets related to drones. We then select the widely used YOLOv7 algorithm as the foundation and conduct a comprehensive analysis of its limitations, proposing a targeted solution. In order to enhance the network’s ability to extract features from small objects, we introduce non-strided convolution modules and integrate modules that utilize attention mechanism principles into the baseline network. Additionally, we improve the semantic information expression for small targets by optimizing the feature fusion process in the network. During training, we adopt the latest Lion optimizer and MPDIoU loss to further boost the overall performance of the network. The improved network achieves impressive results, with mAP50 scores of 56.8% and 94.6% on the VisDrone2019 and NWPU VHR-10 datasets, respectively, particularly in detecting small objects.

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Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Cited by 18 publications

References 119 publications

Availability of Services in Wireless Sensor Network with Aerial Base Station Placement

Availability of Services in Wireless Sensor Network with Aerial Base Station Placement

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

A Small Object Detection Method for Drone-Captured Images Based on Improved YOLOv7

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