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  • Open Access

    ARTICLE

    3D Model Construction and Ecological Environment Investigation on a Regional Scale Using UAV Remote Sensing

    Chao Chen1,2, Yankun Chen3, Haohai Jin4, Li Chen5,*, Zhisong Liu3, Haozhe Sun4, Junchi Hong4, Haonan Wang4, Shiyu Fang4, Xin Zhang2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1655-1672, 2023, DOI:10.32604/iasc.2023.039057 - 21 June 2023

    Abstract The acquisition of digital regional-scale information and ecological environmental data has high requirements for structural texture, spatial resolution, and multiple parameter categories, which is challenging to achieve using satellite remote sensing. Considering the convenient, facilitative, and flexible characteristics of UAV (unmanned air vehicle) remote sensing technology, this study selects a campus as a typical research area and uses the Pegasus D2000 equipped with a D-MSPC2000 multi-spectral camera and a CAM3000 aerial camera to acquire oblique images and multi-spectral data. Using professional software, including Context Capture, ENVI, and ArcGIS, a 3D (three-dimensional) campus model, a digital More >

  • Open Access

    ARTICLE

    Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

    Shuja Ali1, Ahmad Jalal1, Mohammed Hamad Alatiyyah2, Khaled Alnowaiser3, Jeongmin Park4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114 - 08 June 2023

    Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal… More >

  • Open Access

    ARTICLE

    Energy Efficient Green Routing for UAVs Ad-Hoc Network

    M. Muthukumar1, Rajasekar Rangasamy2, Irshad Hussain3,4, Salman A. AlQahtani4,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1111-1127, 2023, DOI:10.32604/iasc.2023.034369 - 29 April 2023

    Abstract The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing (S-ELHR), a low latency routing protocol that aims to provide a stable mechanism for routing in unmanned aerial vehicles (UAV). The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set (CDS) using a parameter known as the Stability Metric (SM). The SM considers the node’s energy usage, connectivity time, and node’s degree. Only the highest SM nodes are chosen to form CDS. Each node declares a Willingness to indicate that it is prepared to serve as a… More >

  • Open Access

    ARTICLE

    Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

    Lemei Da1, Hai Liang1,*, Yong Ding1,2, Yujue Wang1, Changsong Yang1, Huiyong Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 879-902, 2023, DOI:10.32604/cmes.2023.026309 - 23 April 2023

    Abstract The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.… More >

  • Open Access

    ARTICLE

    Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

    Maryam Alhassan, Abdul Raouf Khan*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2727-2740, 2023, DOI:10.32604/cmc.2023.037129 - 31 March 2023

    Abstract Preserving privacy is imperative in the new unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture to ensure that sensitive information is protected and kept secure throughout the communication. Simultaneously, efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained. This study proposes a lightweight and efficient authentication scheme for the UAV-assisted MEC environment. The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting… More >

  • Open Access

    ARTICLE

    Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

    Shakti Raj Chopra1, Parulpreet Singh1, Ahmed Alhussen2,*, Nitin Mittal3, MohdAnul Haq4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3189-3201, 2023, DOI:10.32604/cmc.2023.030061 - 31 March 2023

    Abstract Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving… More >

  • Open Access

    ARTICLE

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

    Feisha Hu1, Qi Wang1,*, Haijian Shao1,2, Shang Gao1, Hualong Yu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732 - 09 March 2023

    Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global More > Graphic Abstract

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

  • Open Access

    ARTICLE

    Energy-Efficient UAVs Coverage Path Planning Approach

    Gamil Ahmed1, Tarek Sheltami1,*, Ashraf Mahmoud1, Ansar Yasar2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3239-3263, 2023, DOI:10.32604/cmes.2023.022860 - 09 March 2023

    Abstract Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most… More >

  • Open Access

    ARTICLE

    Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

    Waleed Aldosari*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1253-1269, 2023, DOI:10.32604/cmc.2023.036111 - 06 February 2023

    Abstract This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a… More >

  • Open Access

    ARTICLE

    Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

    Mohamed Amine Ouamri1,2, Reem Alkanhel3,*, Daljeet Singh4, El-sayed M. El-kenaway5, Sherif S. M. Ghoneim6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 73-92, 2023, DOI:10.32604/csse.2023.034461 - 20 January 2023

    Abstract Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive… More >

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