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Journal articles on the topic 'Drone architecture'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Gao, Ying, Yangliang Liu, Quansi Wen, Hongliang Lin, and Yijian Chen. "Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G." Sensors 20, no. 21 (2020): 6209. http://dx.doi.org/10.3390/s20216209.

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With the development of the Internet of Things (IoT), the number of drones, as a consumer-level IoT device, is rapidly increasing. The existence of a large number of drones increases the risk of misoperation during manual control. Therefore, it has become an inevitable trend to realize drone flying automation. Drone flying automation mainly relies on massive drone applications and services as well as third-party service providers, which not only complicate the drone network service environment but also raise some security and privacy issues. To address these challenges, this article proposes a

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Wadhwa, Shivani, Divya Gupta, Shalli Rani, Maha Driss, and Wadii Boulila. "Empowering drones in vehicular network through fog computing and blockchain technology." PLOS ONE 20, no. 1 (2025): e0314420. https://doi.org/10.1371/journal.pone.0314420.

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The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems. Despite the increasing demand for drone flying automation, there is a significant absence of a comprehensive drone network service architecture tailored for secure and efficient operations of drones. This research

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Du, Jiang, Qiang Wei, Yisen Wang, and Xingyu Bai. "DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis." Drones 9, no. 2 (2025): 110. https://doi.org/10.3390/drones9020110.

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With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-arc

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Yin, Tung. "Game of Drones." Texas A&M Law Review 2, no. 4 (2015): 635–73. http://dx.doi.org/10.37419/lr.v2.i4.3.

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This Article considers the threat posed by drone terrorism and the technological and legal issues involved in setting up defensive responses. First, the Article provides an overview of current drone technology, legal views on weaponized military drones, and a discussion of the potential appeal of drones as vehicles for terrorism. Next, it discusses the current laws and regulations pertaining to private drones. The Article then lays out the three challenges to defending against drone terrorism: detecting potentially hostile drones, identifying them, and destroying or neutralizing them. Finally,

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Takva, Çağatay, and Zeynep Yeşim İlerisoy. "Flying Robot Technology (Drone) Trends: A Review in the Building and Construction Industry." Architecture, Civil Engineering, Environment 16, no. 1 (2023): 47–68. http://dx.doi.org/10.2478/acee-2023-0004.

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Abstract With the emergence of Industry 4.0, the use of robotic technologies is among today’s trends. The innovations that this revolution brought to robotic construction in the fields of communication, control, and software also improve flying robot technology. In the study, the place and importance of drone technology, which is one of the flying robot technologies at the intersection of Industry 4.0 and AEC (architecture, engineering, and construction) industry, in architecture is defined. The research aims to analyze the digital fabrication studies with drone technology in the field of arch

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Abdullah, Abdullah, and Shailendra Mishra. "ML-based Intrusion Detection for Drone IoT Security." Journal of Cybersecurity and Information Management 14, no. 1 (2024): 64–78. http://dx.doi.org/10.54216/jcim.140105.

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The integration of drones into various industries brings about cybersecurity challenges due to their reliance on internet connectivity. To address this, we propose a comprehensive cybersecurity architecture leveraging machine learning (ML) algorithms and Internet of Things (IoT) technologies within the Internet of Drones (IoD) framework. Our architecture employs IoT-enabled sensors strategically placed across the drone ecosystem to collect and analyze data on system behaviors, communication patterns, and environmental variables. This data is then processed by a centralized platform equipped wi

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Alturki, Nazik, Turki Aljrees, Muhammad Umer, et al. "An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection." Sensors 23, no. 16 (2023): 7154. http://dx.doi.org/10.3390/s23167154.

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The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current archit

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Appelbaum, Deniz, and Robert A. Nehmer. "Using Drones in Internal and External Audits: An Exploratory Framework." Journal of Emerging Technologies in Accounting 14, no. 1 (2017): 99–113. http://dx.doi.org/10.2308/jeta-51704.

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ABSTRACT: Recently the FAA relaxed restrictions on the use of drones or Unmanned Aircraft Systems (UASs) for commercial purposes. Markets for commercial drone use are in the technology trigger phase of the Gartner Group's Hyper Cycle, with developments occurring rapidly in real estate, agriculture (farming), the film industry, insurance, and other areas. Examination and inspection applications of drones have been proposed in heavy industry and cell tower inspection. Previous research suggests an incremental structure for implementing technological innovations such as continuous auditing (CA).

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Mirzaeinia and Hassanalian. "Minimum-Cost Drone‒Nest Matching through the Kuhn‒Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement." Aerospace 6, no. 11 (2019): 125. http://dx.doi.org/10.3390/aerospace6110125.

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The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn‒Munkres (Hu

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Truong, Xuan Tung. "DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING." Journal of Military Science and Technology, no. 73 (June 15, 2021): 10–19. http://dx.doi.org/10.54939/1859-1043.j.mst.73.2021.10-19.

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The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained mod

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Langen, Tommy, Vimala Nunavath, and Ole Henrik Dahle. "A Conceptual Framework Proposal for a Noise Modelling Service for Drones in U-Space Architecture." International Journal of Environmental Research and Public Health 19, no. 1 (2021): 223. http://dx.doi.org/10.3390/ijerph19010223.

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In recent years, there has been a rapid growth in the development and usage of flying drones due to their diverse capabilities worldwide. Public and private sectors will actively use drone technology in the logistics of goods and transporting passengers in the future. There are concerns regarding privacy and noise exposure in and around the rural and urban environment with the rapid expansion. Further, drone noise could affect human health. European Union has defined a service-orientated architecture to provide air traffic management for drones, called U-space. However, it lacks a noise modell

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Kolamunna, Harini, Thilini Dahanayaka, Junye Li, et al. "DronePrint." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (2021): 1–31. http://dx.doi.org/10.1145/3448115.

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With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain un

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Hummel, Karin, Manuela Pollak, and Johannes Krahofer. "A Distributed Architecture for Human-Drone Teaming: Timing Challenges and Interaction Opportunities." Sensors 19, no. 6 (2019): 1379. http://dx.doi.org/10.3390/s19061379.

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Drones are expected to operate autonomously, yet they will also interact with humans to solve tasks together. To support civilian human-drone teams, we propose a distributed architecture where sophisticated operations such as image recognition, coordination with humans, and flight-control decisions are made, not on-board the drone, but remotely. The benefits of such an architecture are the increased computational power available for image recognition and the possibility to integrate interfaces for humans. On the downside, communication is necessary, resulting in the delayed reception of comman

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Chang, Sheng-I., and Jih-Gau Juang. "UAV Control Based on Pattern Recognition in Aquaculture Application." Aerospace 11, no. 4 (2024): 302. http://dx.doi.org/10.3390/aerospace11040302.

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This study proposes a drone application for the net cage aquaculture industry. A visual control structure is applied to the drone to obtain water-quality information surrounding the net cages. This study integrates a hexacopter, camera, onboard computer, flight control board, servo motor, and global positioning system’s auto-cruise function to adjust the drone position and control the servo motor retractable sensor to reach the desired target at an accurate location. In object identification, a deep learning neural network is used to identify the net cages. An onboard computer calculates the h

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Dorave, Jayantkumar, and Ritesh Sadiwala. "Uplink and Downlink Variation in Drone Technology for Cloud, Edge, Fog and Smart Dust Integrated IoT Architecture: Demonstrated Over WSNs." Journal of Physics: Conference Series 2089, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/2089/1/012023.

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Abstract IoT concepts are heavily applicable in drone communication integrated with different network architecture for optimization. Distributing the burden allows more IoT devices to execute calculations, rather than everything being done on the cloud. There are numerous IoT designs that have emerged as a result of this. By relocating calculations away from the cloud, these designs make use of the enhanced processing capacity of the devices. Based on our needs, we’ve limited it down to four architectures, each of which we have discussed for optimized flow useful in drone technology. We have a

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Motahari Rad, Zahra, and Jonathan Liscouët. "Integrating Null Controllability and Model-Based Safety Assessment for Enhanced Reliability in Drone Design." Modelling 5, no. 3 (2024): 1009–30. http://dx.doi.org/10.3390/modelling5030053.

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The increasing use of drones for safety-critical applications, particularly beyond visual lines of sight and over densely populated areas, necessitates safer and more reliable designs. To address this need, this paper introduces a novel methodology integrating Null Controllability with the Model-Based Safety Assessment (MBSA) framework AltaRica 3.0 to optimize propulsor configurations and system architectures. The main advancement of this method lies in the automation of reliability modeling and the integration of controllability assessment, eliminating restrictions on the types of propulsor c

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Sahithi, N. "Drone Detection and Classification Using Music (Multiple Signal Classification) Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 767–71. http://dx.doi.org/10.22214/ijraset.2023.53462.

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Abstract: The rapid augmentation of drones in different sectors has raised concerns regarding privacy, security, and safety. This project proposes a novel drone detection and classification approach by integrating the YOLOv5 framework with a MUSICbased algorithm to address the above issues. The aim is to postulate an efficient and accurate model capable of identifying and classifying different types of drones in real time. The project leverages the YOLOv5 architecture incorporated with the MUSIC algorithm for superior speed and accuracy. By training YOLOv5 on a large dataset of drone images, t

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Widhalm, Peter, Ulrike Ritzinger, Natalie Prüggler, et al. "Community Drones: A Concept Study on Shared Drone Services." Drones 9, no. 2 (2025): 107. https://doi.org/10.3390/drones9020107.

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The growing demand for civil drone services across diverse sectors necessitates more efficient and accessible operational models. This paper introduces the Community Drones model, a shared infrastructure framework designed to coordinate regional drone services through automated resource sharing. By consolidating tasks and resources, this approach enhances drone utilization and reduces operational costs and user effort while ensuring high service quality, reliability, and flexibility. At the core of the model is an advanced mission planning system that automates flight scheduling, risk assessme

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Çetin, Ender, Cristina Barrado, and Enric Pastor. "Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning." Sensors 20, no. 8 (2020): 2320. http://dx.doi.org/10.3390/s20082320.

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Counter-drone technology by using artificial intelligence (AI) is an emerging technology and it is rapidly developing. Considering the recent advances in AI, counter-drone systems with AI can be very accurate and efficient to fight against drones. The time required to engage with the target can be less than other methods based on human intervention, such as bringing down a malicious drone by a machine-gun. Also, AI can identify and classify the target with a high precision in order to prevent a false interdiction with the targeted object. We believe that counter-drone technology with AI will b

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Kravchuk, Serhii, and Iryna Kravchuk. "AUTHENTICATION OF WIRELESS SYSTEMS BASED ON A DRONE SWARM AS A COMPONENT OF THE 5G RADIO ACCESS NETWORK ARCHITECTURE." Information and Telecommunication Sciences, no. 1 (June 28, 2024): 18–27. http://dx.doi.org/10.20535/2411-2976.12024.18-27.

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Background. When establishing security measures for telecommunication networks involving unmanned aerial vehicles (UAVs), characteristics such as high scalability, device diversity, and high mobility should be considered. Authentication is a fundamental property that allows a UAV network to establish secure communication between its core components. Authentication also protects the UAV network from attackers impersonating legitimate UAVs. UAV authentication can further secure the communication channel by preventing impersonation and replay attacks. The design of UAV access control schemes, suc

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Rahman, F., and Ashu Nayak. "Drone-Based 3D Modeling for the Conservation and Restoration of Heritage Buildings." Architecture Image Studies 6, no. 1 (2025): 46–55. https://doi.org/10.62754/ais.v6i1.105.

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The technology to assess this nation's local architectural legacy must be lightweight, effective, and adaptable. Economical drones and Structure-from-Motion (SM) methods can fulfill these requirements. Recent research utilizing nadir photos from economical drones and SM methods has demonstrated favorable metric outcomes (achieving centimeter-level precision) modeling two-dimensional items (properties, rooftops, and facades). Constructing a comprehensive 3D model of past buildings is highly desirable, necessitating a camera system capable of producing nadir and side views with varying starting

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Mahajan, Gayatri. "Applications of Drone Technology in Construction Industry: A Study 2012-2021." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 224–39. http://dx.doi.org/10.35940/ijeat.a3165.1011121.

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Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (i) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. Thi

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Kerai Divyakumar Pravinbhai, AnkitKumar S. Patel, Jayraj V. Solanki, and Vikram Patel. "Drone Technology in Construction Industry." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 08 (2024): 2215–21. http://dx.doi.org/10.47392/irjaeh.2024.0302.

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Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (I) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. Thi

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Gayatri, Mahajan. "Applications of Drone Technology in Construction Industry: A Study 2012-2021." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 224–39. https://doi.org/10.35940/ijeat.A3165.1011121.

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Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (i) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. Thi

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Go, Yong-Guk, Ho-San Kang, Jong-Won Lee, Mun-Su Yu, and Soo-Mi Choi. "Multi-User Drone Flight Training in Mixed Reality." Electronics 10, no. 20 (2021): 2521. http://dx.doi.org/10.3390/electronics10202521.

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The development of services and applications involving drones is promoting the growth of the unmanned-aerial-vehicle industry. Moreover, the supply of low-cost compact drones has greatly contributed to the popularization of drone flying. However, flying first-person-view (FPV) drones requires considerable experience because the remote pilot views a video transmitted from a camera mounted on the drone. In this paper, we propose a remote training system for FPV drone flying in mixed reality. Thereby, beginners who are inexperienced in FPV drone flight control can practice under the guidance of r

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Schopferer, Simon, Philipp Schitz, Mark Spiller, et al. "Offshore Wind Farm Delivery with Autonomous Drones: A Holistic View of System Architecture and Onboard Capabilities." Drones 9, no. 4 (2025): 295. https://doi.org/10.3390/drones9040295.

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Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission

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Çetin, Ender, Cristina Barrado, and Enric Pastor. "Countering a Drone in a 3D Space: Analyzing Deep Reinforcement Learning Methods." Sensors 22, no. 22 (2022): 8863. http://dx.doi.org/10.3390/s22228863.

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Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people’s privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. Ho

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Nugroho, Eddy Prasetyo, Taufik Djatna, Imas Sukaesih Sitanggang, et al. "Metadata Modeling of LoRa Based Payload Information for Precision Agriculture Tea Plantation." Scientific Journal of Informatics 10, no. 2 (2023): 217–28. http://dx.doi.org/10.15294/sji.v10i2.43432.

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Purpose: The purpose of this study is to model the metadata of Payload Information on Agriculture Drones which consists of the results of images computational and the Onboard system of the Drone.Methods: The stages of the research were carried out with the process of forming Payload information metadata from the Agriculture Drone with sensors/actuators based on the architecture and computing with Image Processing or Computer Vision on the camera captures. This study describes the metadata modeling process formed from the Internet of Things system with Drone and GCS communication based on the L

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T.S.R., Krishna Prasad, Mahesh Kiran P., Hima Sameera M., Sri Sai Nachiketha P., and Mukesh Vamsi. M. "Drone Movement Detection Network using Raspberry Pi." International Journal of Innovative Science and Research Technology 8, no. 3 (2023): 1765–71. https://doi.org/10.5281/zenodo.7797284.

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This research paper proposes a system for detecting drones that use Raspberry Pi as its primary computing platform and implements the SSD MobileNetv2 architecture. The proposed approach involves training a machine learning model using deep learning and convolutional neural network algorithms. The SSD Mobilenetv2 architecture is proposed due to its accuracy and optimal performance in real-time object detection. The dataset includes images of numerous drones in various positions. The dataset has undergone image augmentations such as flipping, blurring, granulation and grayscale conversion, at ra

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Aldossary, Mohammad, Ibrahim Alzamil, and Jaber Almutairi. "Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications." Drones 9, no. 1 (2025): 46. https://doi.org/10.3390/drones9010046.

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Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCA

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Atashyan, Agit, Artyom Lazyan, Davit Hayrapetyan, and Vahagn Poghosyan. "Implementation of an Automata Mechanism for a Self-Organizing Swarm of Drones Platform." Mathematical Problems of Computer Science 63 (June 1, 2025): 71–80. https://doi.org/10.51408/1963-0133.

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Drone technology has enabled major advancements in autonomous systems, particularly in swarm robotics. This paper presents a novel automation technique aimed at enhancing the efficiency, adaptability, and robustness of self-organizing drone swarms. The system uses decentralized control algorithms and robust communication protocols to enable real-time adaptive learning and decision-making among drones. Each drone acts as an autonomous agent, adjusting its behavior based on environmental inputs and interactions with other drones. A hybrid communication model blending peer-to-peer and cluster-bas

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Al-Qubaydhi, Nader, Abdulrahman Alenezi, Turki Alanazi, et al. "Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning." Electronics 11, no. 17 (2022): 2669. http://dx.doi.org/10.3390/electronics11172669.

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Drones/unmanned aerial vehicles (UAVs) have recently grown in popularity due to their inexpensive cost and widespread commercial use. The increased use of drones raises the possibility that they may be employed in illicit activities such as drug smuggling and terrorism. Thus, drone monitoring and automated detection are critical for protecting restricted areas or special zones from illicit drone operations. One of the most challenging difficulties in drone detection in surveillance videos is the apparent likeness of drones against varied backdrops. This paper introduces an automated image-base

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Nwaogu, Janet Mayowa, Yang Yang, Albert P. C. Chan, and Xiaowei Wang. "Enhancing Drone Operator Competency within the Construction Industry: Assessing Training Needs and Roadmap for Skill Development." Buildings 14, no. 4 (2024): 1153. http://dx.doi.org/10.3390/buildings14041153.

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Industry 5.0 is expected to solve the issue of job insecurity and reluctance to adopt emerging technologies in Industry 4.0 through increased workforce participation. To achieve that, proactive training should be afforded to enable the workforce to co-work with new technologies. Drones are an emerging technology increasingly adopted in construction, which has enormous data collection and safety implications when operational skills are inadequate. Yet, current drone training programs appear to be generic, and their ability to equip operators for flying tasks is questioned. This study aims to an

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Ren, Junxiao, Zijia Wang, Ji Zhao, and Xinggui Liu. "A Drone Sound Recognition Approach Using Adaptive Feature Fusion and Cross-Attention Feature Enhancement." Electronics 14, no. 8 (2025): 1491. https://doi.org/10.3390/electronics14081491.

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With the rapid development and widespread application of drones across various fields, drone recognition and classification at medium and long distances have become increasingly important yet challenging tasks. This paper proposes a novel network architecture called AECM-Net, which integrates an adaptive feature fusion (AF) module, an efficient channel attention (ECA), and a criss-cross attention (CCA) mechanism-enhanced multi-scale feature extraction module (MSC). The network employs both Mel-frequency cepstral coefficients (MFCCs) and Gammatone cepstral coefficients (GFCC) as input features,

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Kim, Song-Kyoo (Amang). "Advanced Drone Swarm Security by Using Blockchain Governance Game." Mathematics 10, no. 18 (2022): 3338. http://dx.doi.org/10.3390/math10183338.

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This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable

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Krovvidi, Maansa, and Ch Mvn Sai Teja Prashanth. "Distributed Cellular Framework for Secure Drone Delivery Services." International Journal of Research in Advent Technology 9, no. 9 (2021): 1–6. http://dx.doi.org/10.32622/ijrat.99202103.

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This paper describes in-depth the architecture of a Distributed Cellular framework for Secure Drone Delivery Services with an additional security layer. Delivery Drones are Unmanned aerial vehicles (UAV) that are responsible for the transportation of goods from one place to another place. In the modern world, with several e-commerce platforms, everything is traded, promoted, advertised online. This causes the need to deliver the packages rapidly and within a stipulated period of time. Unmanned aerial vehicles or drones come into the picture to retort the above-mentioned issue. Security nowaday

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Pergola, Pierpaolo, and Vittorio Cipolla. "Mission architecture for Mars exploration based on small satellites and planetary drones." International Journal of Intelligent Unmanned Systems 4, no. 3 (2016): 142–62. http://dx.doi.org/10.1108/ijius-12-2015-0014.

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Purpose The purpose of this paper is to deal with the study of an innovative unmanned mission to Mars, which is aimed at acquiring a great amount of detailed data related to both Mars’ atmosphere and surface. Design/methodology/approach The Mars surface exploration is conceived by means of a fleet of drones flying among a set of reference points (acting also as entry capsules and charging stations) on the surface. The three key enabling technologies of the proposed mission are the use of small satellites (used in constellation with a minimum of three), the use of electric propulsion systems fo

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McCarthy, Tim, Lars Pforte, and Rebekah Burke. "Fundamental Elements of an Urban UTM." Aerospace 7, no. 7 (2020): 85. http://dx.doi.org/10.3390/aerospace7070085.

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Urban airspace environments present exciting new opportunities for delivering drone services to an increasingly large global market, including: information gathering; package delivery; air-taxi services. A key challenge is how to model airspace environments over densely populated urban spaces, coupled with the design and development of scalable traffic management systems that may need to handle potentially hundreds to thousands of drone movements per hour. This paper explores the background to Urban unmanned traffic management (UTM), examining high-level initiatives, such as the USA’s Unmanned

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Fedorovych, Oleg, Dmytro Kritskiy, Leonid Malieiev, Kseniia Rybka, and Andrii Rybka. "Military logistics planning models for enemy targets attack by a swarm of combat drones." Radioelectronic and Computer Systems 2024, no. 1 (2024): 207–16. http://dx.doi.org/10.32620/reks.2024.1.16.

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This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistic

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Majeed, Rizwan, Nurul Azma Abdullah, Muhammad Faheem Mushtaq, Muhammad Umer, and Michele Nappi. "Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier." Electronics 10, no. 23 (2021): 2926. http://dx.doi.org/10.3390/electronics10232926.

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Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone req

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Neelakanta, Perambur, and Dolores De Groff. "Radar Detection of Loitering Drones Via Artificial Neural Networks: A Generative AI Software Support." Transactions on Machine Learning and Artificial Intelligence 12, no. 5 (2024): 01–16. https://doi.org/10.14738/tecs.125.17572.

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The focused effort of this study refers to developing an ANN-based generative AI software towards radar detection of loitering drones in a combat area. Relevant heuristics can provide an adjunct support to a surveillance radar system that remains vulnerable to threats on attacks by low-observable loitering drones. Detection of such drones is necessary for proactive counter actions. In general, a loitering drone implies a suicide munition deployed as an exploding device mimicking kamikaze-style operation at war zones. It is intended to crash on to a vulnerable target such as, a surveillance rad

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Bassolillo, Salvatore Rosario, Egidio D’Amato, Immacolata Notaro, Luca D’Agati, Giovanni Merlino, and Giuseppe Tricomi. "Bridging ACO-Based Drone Logistics and Computing Continuum for Enhanced Smart City Applications." Drones 9, no. 5 (2025): 368. https://doi.org/10.3390/drones9050368.

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In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and auto

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Alarcón, Víctor, Manuel García, Francisco Alarcón, et al. "Procedures for the Integration of Drones into the Airspace Based on U-Space Services." Aerospace 7, no. 9 (2020): 128. http://dx.doi.org/10.3390/aerospace7090128.

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A safe integration of drones into the airspace is fundamental to unblock the potential of drone applications. U-space is the drone traffic management solution for Europe, intended to handle a large number of drones in the airspace, especially at very low level (VLL). This paper presents the procedures we have designed and tested in real flights in the SAFEDRONE European project to pave the way for a safe integration of drones into the airspace using U-space services. We include three important aspects: Design of procedures related to no-fly zones, ensure separation with manned aircraft, and au

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Sutikno, Tole. "Quantum drones and the future of military warfare." International Journal of Informatics and Communication Technology (IJ-ICT) 12, no. 3 (2024): 293. http://dx.doi.org/10.11591/ijict.v12i3.pp293-299.

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The advent of drones has significantly impacted military warfare, providing improved reconnaissance, surveillance, and target acquisition (RSTA), cost savings, increased convenience, safety, and flexibility. A layered network control architecture, known as the internet of drones (IoD), coordinates drone access to controlled airspace and offers navigation services. Various systems, including wireless sensor networks (WSN) and drones directed to an expanded controlling zone, integrate with IoD to improve connection performance. This paper provides an overview of the IoD and the internet of quant

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Tole, Sutikno. "Quantum drones and the future of military warfare." International Journal of Informatics and Communication Technology 12, no. 3 (2023): 293–99. https://doi.org/10.11591/ijict.v12i3.pp293-299.

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The advent of drones has significantly impacted military warfare, providing improved reconnaissance, surveillance, and target acquisition (RSTA), cost savings, increased convenience, safety, and flexibility. A layered network control architecture, known as the internet of drones (IoD), coordinates drone access to controlled airspace and offers navigation services. Various systems, including wireless sensor networks (WSN) and drones directed to an expanded controlling zone, integrate with IoD to improve connection performance. This paper provides an overview of the IoD and the internet of quant

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Ahmad, Hijaz, Muhammad Farhan, and Umar Farooq. "Computer Vision Techniques for Military Surveillance Drones." Wasit Journal of Computer and Mathematics Science 2, no. 2 (2023): 56–63. http://dx.doi.org/10.31185/wjcms.148.

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Commercial unmanned aerial vehicles (UAVs), also referred to as drones, have proliferated recently, raising concerns about security threats and the need for effective countermeasures. To address these concerns, various technologies have been explored, including radar, acoustics, and RF signal analysis. However, computer vision, particularly deep learning approaches, has emerged as a robust and widely used method for autonomous drone identification. The goal of this research is to create an autonomous drone identification and surveillance system that makes use of a mix of static wide-angle came

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Al-Dhaqm, Arafat, Richard A. Ikuesan, Victor R. Kebande, Shukor Razak, and Fahad M. Ghabban. "Research Challenges and Opportunities in Drone Forensics Models." Electronics 10, no. 13 (2021): 1519. http://dx.doi.org/10.3390/electronics10131519.

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The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digit

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Alam, Mehbub, Nurzaman Ahmed, Rakesh Matam, and Ferdous A. Barbhuiya. "IEEE 802.11ah-Enabled Internet of Drone Architecture." IEEE Internet of Things Magazine 5, no. 1 (2022): 174–78. http://dx.doi.org/10.1109/iotm.005.2100099.

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Evdokimenkov, V. N., R. V. Kim, and S. S. Popov. "Architecture of Software for Simulating Drone Operation." Russian Engineering Research 41, no. 12 (2021): 1209–12. http://dx.doi.org/10.3103/s1068798x21120121.

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Unlu, Eren, Emmaneul Zenou, Nicolas Riviere, and Paul-Edouard Dupouy. "An autonomous drone surveillance and tracking architecture." Electronic Imaging 2019, no. 15 (2019): 35–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.15.avm-035.

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