Special Issues
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Intelligent Soft Computing Techniques for Enhancing Wireless Networks with Unmanned Aerial Vehicles

Submission Deadline: 30 April 2025 View: 628 Submit to Special Issue

Guest Editors

Prof. Giovanni Pau

Email: giovanni.pau@unikore.it

Affiliation: Department of Engineering and Architecture, Kore University of Enna – 94100 Enna, Italy

Homepage:

Research Interests: Wireless sensor networks; fuzzy logic; machine learning

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Summary

This special issue aims to investigate the innovative application of soft computing techniques—such as fuzzy logic, neural networks, and evolutionary algorithms—in optimizing the performance, reliability, and security of wireless networks, specifically in the context of Unmanned Aerial Vehicles (UAVs). As UAV technology continues to revolutionize various sectors, including logistics, surveillance, and environmental monitoring, there is an urgent need for advanced methodologies to address the unique challenges posed by the integration of UAVs into wireless communication networks.


Key areas of focus for this special issue include, but are not limited to:

1. UAV-Assisted Network Management: Developing soft computing approaches that leverage UAVs for dynamic network management, including the optimization of coverage and connectivity in remote or disaster-stricken areas.

2. Quality of Service (QoS) Enhancement: Investigating the use of machine learning and fuzzy logic to ensure QoS in UAV-based communication systems, facilitating efficient data transmission and minimizing latency in real-time applications.

3. Security and Privacy in UAV Networks: Exploring soft computing techniques aimed at enhancing the security of UAV communication networks, including anomaly detection, secure data transmission, and threat mitigation strategies.

4. Energy-Efficient UAV Operations: Proposing novel soft computing algorithms for optimizing energy consumption in UAV operations, particularly in applications where extended flight time and battery life are critical.

5. Collaborative UAV Networks: Studying the application of machine learning and soft computing for enabling coordination and collaboration among multiple UAVs, improving data sharing and network resilience.

6. Real-Time Data Processing and Decision Making: Focusing on the integration of soft computing techniques for real-time data analysis and decision-making in UAV operations, particularly in dynamic environments where rapid responses are required.


By providing a dedicated platform for research that combines soft computing and machine learning with UAV technology in the context of wireless networks, this special issue aims to attract high-quality submissions from both academia and industry. The innovative integration of these technologies has the potential to significantly advance the field of wireless communication, addressing current challenges and paving the way for future developments in UAV applications.


Keywords

UAV-Assisted Network Management; Quality of Service (QoS) Enhancement; Security and Privacy in UAV Networks; Energy-Efficient UAV Operations; Collaborative UAV Networks; Real-Time Data Processing and Decision Makin

Published Papers


  • Open Access

    ARTICLE

    YOLO-S3DT: A Small Target Detection Model for UAV Images Based on YOLOv8

    Pengcheng Gao, Zhenjiang Li
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873
    (This article belongs to the Special Issue: Intelligent Soft Computing Techniques for Enhancing Wireless Networks with Unmanned Aerial Vehicles)
    Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >

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