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Table of Content

Edge Computing Enabled Internet of Drones

Submission Deadline: 30 December 2024 Submit to Special Issue

Guest Editors

Prof. Wei Wu, Nanjing University of Posts and Telecommunications, China
Prof. Fuhui Zhou, Nanjing University of Aeronautics and Astronautics, China
Prof. Leibing Yan, Henan Institute of Technology, China
Prof. Kai Kit Wong, University College London, Londan, United Kingdom
Prof. Rose Qingyang Hu, Utah State University, Logan, Utah, United States

Summary

Mobile edge computing (MEC) enabled unmanned aerial vehicle (UAV) communication has emerged as a promising technique for wireless devices to realize low latency and high reliability communication and computation services in a more flexible and cost-effective manner. However, the small-scale UAVs are incompetence in handling more complex missions, such as earth monitoring, precision agriculture and large-scale military deployment. As a result, edge computing enabled UAV swarm intelligence has attracted great attention from academia and industry in recent years. It is envisioned that edge computing enabled UAV swarm intelligence can provide strong support for us to embrace the forthcoming era of “Internet of Drones (IoD)” and gain wide popularity in supporting future human activities. In order to facilitate the implementation of edge computing enabled UAV swarm intelligence, several preliminary research work have been carried out including resource allocation of edge computing enabled UAV swarm intelligence and dynamic spectrum management of edge computing enabled UAV swarm intelligence.

Although these emerging issues have drawn considerable attention and have been studied recently, there are still many open theoretical and practical problems to be addressed. Specifically, in order to ensure low execution latency and high energy efficiency of edge computing enabled UAV swarm intelligence, how to reduce UAV-to-ground and UAV-to-UAV interference, need to be further investigated. In addition, note that the severe intra-swarm wireless interference, the uncertainty of wireless channel and data processing latency will inevitably cause response delay of UAV, which impairs the stability of the UAV swarm. Therefore, more research efforts are needed to investigate the effective robust automatic networking technologies for keeping stability of large-scale UAV swarm. Furthermore, computing task sharing has a huge risk of privacy leakage, which prompts the computational security in the edge computing enabled UAV swarm intelligence to be an attentional issue.


The aim of this special issue is to provide a new comprehensive overview on UAV swarm and create more ideas on edge computing enabled UAV swarm intelligence, which will bring together researchers from academia, industry and governmental agencies to promote the research and development needed to address the major challenges that pertain to this cutting-edge research topic.

 

Potential topics include but are not limited to:

• Performance analysis for edge computing enabled IoD

• Implementation issues in edge computing enabled IoD

• Resource allocation strategies for multi-UAV communication

• Multi-antenna techniques for edge computing enabled IoD

• Multi-access techniques for UAV swarm intelligence

• Energy-efficient cooperative sensing techniques for UAV swarm intelligence

• Physical layer security for edge computing enabled UAV swarm intelligence

• Enhanced 3D spectrum sensing for UAV swarm intelligence

• Machine learning and deep learning for edge computing enabled UAV swarm intelligence

• Federated learning for edge computing enabled UAV swarm intelligence

• Mission oriented multi-UAV automatic networking

• Energy-efficient UAV path planning/computation offloading

• Spectrum-efficient techniques for edge computing enabled IoD

• Intelligent reflection surface assisted resource allocation/edge computing

• UAV assisted task oriented intelligent semantic communication

• Other emerging techniques for UAV swarm-enabled edge computing


Keywords

Internet of drones, edge computing, energy efficiency, resource allocation, performance analysis, computing security

Published Papers


  • Open Access

    ARTICLE

    Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

    Ping Li, Kefeng Guo, Feng Zhou, Xueling Wang, Yuzhen Huang
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1849-1864, 2024, DOI:10.32604/cmes.2023.029801
    (This article belongs to the Special Issue: Edge Computing Enabled Internet of Drones)
    Abstract Integrated satellite unmanned aerial vehicle relay networks (ISUAVRNs) have become a prominent topic in recent years. This paper investigates the average secrecy capacity (ASC) for reconfigurable intelligent surface (RIS)-enabled ISUAVRNs. Especially, an eve is considered to intercept the legitimate information from the considered secrecy system. Besides, we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent. Furthermore, to gain insightful results of the major parameters on the ASC in high signal-to-noise ratio regime, the approximate investigations are further gotten, which give an efficient method to value More >

  • Open Access

    ARTICLE

    Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

    Ying Zhang, Weiming Niu, Supu Xiu, Guangchen Mu
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1865-1884, 2024, DOI:10.32604/cmes.2023.030114
    (This article belongs to the Special Issue: Edge Computing Enabled Internet of Drones)
    Abstract In this paper, we investigate the energy efficiency maximization for mobile edge computing (MEC) in intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) communications. In particular, UAV can collect the computing tasks of the terrestrial users and transmit the results back to them after computing. We jointly optimize the users’ transmitted beamforming and uploading ratios, the phase shift matrix of IRS, and the UAV trajectory to improve the energy efficiency. The formulated optimization problem is highly non-convex and difficult to be solved directly. Therefore, we decompose the original problem into three sub-problems. We first More >

  • Open Access

    ARTICLE

    Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

    Baofeng Ji, Ying Wang, Weixing Wang, Shahid Mumtaz, Charalampos Tsimenidis
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1885-1905, 2024, DOI:10.32604/cmes.2023.030872
    (This article belongs to the Special Issue: Edge Computing Enabled Internet of Drones)
    Abstract The utilization of mobile edge computing (MEC) for unmanned aerial vehicle (UAV) communication presents a viable solution for achieving high reliability and low latency communication. This study explores the potential of employing intelligent reflective surfaces (IRS) and UAVs as relay nodes to efficiently offload user computing tasks to the MEC server system model. Specifically, the user node accesses the primary user spectrum, while adhering to the constraint of satisfying the primary user peak interference power. Furthermore, the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes, namely time… More >

  • Open Access

    ARTICLE

    IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

    Ying Zhang, Weiming Niu, Leibing Yan
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 885-902, 2024, DOI:10.32604/cmes.2023.029234
    (This article belongs to the Special Issue: Edge Computing Enabled Internet of Drones)
    Abstract In this paper, we consider mobile edge computing (MEC) networks against proactive eavesdropping. To maximize the transmission rate, IRS assisted UAV communications are applied. We take the joint design of the trajectory of UAV, the transmitting beamforming of users, and the phase shift matrix of IRS. The original problem is strong non-convex and difficult to solve. We first propose two basic modes of the proactive eavesdropper, and obtain the closed-form solution for the boundary conditions of the two modes. Then we transform the original problem into an equivalent one and propose an alternating optimization (AO) More >

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