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Drones
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Advances of Drones in Green Internet-of-Things
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Advances of Drones in Green Internet-of-Things

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 4608

Special Issue Editors

Dr. Bo Rong

E-Mail Website
Guest Editor
Communications Research Centre Canada Ottawa, ON K2H 8S2, Canada
Interests: 5G/6G; wireless communications; wireless networking; IoT; UAV
Prof. Dr. Michel Kadoch

E-Mail Website
Guest Editor
Department of Electrical Engineering, Ecole de technologie superieure Montreal, QC H3C 1K3, Canada
Interests: IoT; wireless sensor network; 5G small cell networks; machine learning

Special Issue Information

Dear Colleagues,

The green Internet of Things (IoT) has emerged as a proactive response to the mounting environmental concerns arising from the rapid proliferation of IoT devices and technologies. As the IoT continues to expand, the escalating demand for connectivity and data-driven applications leads to heightened energy consumption and increased strain on resources. In propelling the development of a sustainable green IoT, drones have garnered significant attention as versatile platforms capable of collecting data from remote or otherwise inaccessible areas, owing to their mobility, agility, and ability to fly at various altitudes. This Special Issue is dedicated to exploring the latest developments of drones in the green IoT, with a specific focus on showcasing innovative solutions that augment their capabilities and applications.

The primary objective of this Special Issue is to establish a platform for researchers, practitioners, and industry experts to exchange their knowledge, experiences, and perspectives regarding the integration of drones in the green IoT. By curating a selection of high-quality research articles and reviews, our goal is to offer a comprehensive overview of the advancements in this field and foster further research and development endeavors.

The scope of this Special Issue encompasses a broad range of topics related to drones in the green IoT. We encourage submissions in areas including but not limited to:

  • Novel visions, concepts and theories of a drone-enhanced green IoT;
  • Promising models, protocols and architectures for a drone-enhanced green IoT;
  • Artificial intelligence and big data-driven designs for a drone-enhanced green IoT;
  • Digital Twin for a drone-enhanced green IoT;
  • Ambient backscatter communications and a symbiotic radio-based, drone-enhanced green IoT;
  • Energy-efficient beamforming, modulation and coding techniques for a drone-enhanced green IoT;
  • Reconfigurable intelligent surface (RIS)/holographic MIMO surface for a drone-enhanced green IoT;
  • RF energy harvesting, simultaneous wireless and information transfer for a drone-enhanced green IoT;
  • Cloud/edge computing and task/data/computation offloading for a drone-enhanced green IoT;
  • Privacy, security and reliability issues for a drone-enhanced green IoT.

Dr. Bo Rong
Prof. Dr. Michel Kadoch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drones
  • green Internet of Things
  • wireless communication
  • optimization algorithms
  • artificial intelligence

Published Papers (5 papers)

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Research

16 pages, 4218 KiB  
Article
A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles
by Lihao Yao, Honglei Qin, Boyun Gu, Guangting Shi, Hai Sha, Mengli Wang, Deyong Xian, Feiqiang Chen and Zukun Lu
Drones 2024, 8(4), 164; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040164 - 20 Apr 2024
Viewed by 317
Abstract
Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV [...] Read more.
Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning. Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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17 pages, 1346 KiB  
Article
Energy-Efficient Device-to-Device Communications for Green Internet of Things Using Unmanned Aerial Vehicle-Mounted Intelligent Reflecting Surface
by Fangqing Tan, Shuo Pang, Yashuai Cao, Hongbin Chen and Tiejun Lv
Drones 2024, 8(4), 122; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040122 - 26 Mar 2024
Viewed by 541
Abstract
The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of [...] Read more.
The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance. Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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22 pages, 595 KiB  
Article
Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT
by Xi Wang, Shuo Shi and Chenyu Wu
Drones 2024, 8(4), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040117 - 22 Mar 2024
Viewed by 574
Abstract
This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network [...] Read more.
This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network functions must be flexibly configured and adjusted based on varying service demands and network situations. This paper presents a model for calculating benefits and an agile algorithm for embedding and migrating SFC based on particle swarm optimization (PSO). The model takes into account multiple factors such as SFC quality, resource utilization, and migration cost. It aims to maximize the SFC benefit and minimize the migration times. The algorithm leverages PSO’s global search and fast convergence to identify the optimal or near-optimal SFC placement and update it when the network state changes. Simulation experiments demonstrate that the proposed method improves network resource efficiency and outperforms existing methods. This paper presents a new idea and method for managing SFC in UAV IoT. Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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16 pages, 8305 KiB  
Article
Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration
by Tao Hong, Yi Li, Chaoqun Fang, Wei Dong and Zhihua Chen
Drones 2024, 8(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8010020 - 12 Jan 2024
Viewed by 1179
Abstract
This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing [...] Read more.
This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security. Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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16 pages, 4063 KiB  
Article
UAV Digital Twin Based Wireless Channel Modeling for 6G Green IoT
by Fei Qi, Weiliang Xie, Lei Liu, Tao Hong and Fanqin Zhou
Drones 2023, 7(9), 562; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7090562 - 01 Sep 2023
Cited by 1 | Viewed by 1397
Abstract
This paper explores the advancements of drones in the context of sixth-generation mobile communication technology (6G) green Internet of Things (IoT) through the utilization of digital twin (DT) technology within unmanned aerial vehicle (UAV) networks. We propose a framework for DT-based UAV applications [...] Read more.
This paper explores the advancements of drones in the context of sixth-generation mobile communication technology (6G) green Internet of Things (IoT) through the utilization of digital twin (DT) technology within unmanned aerial vehicle (UAV) networks. We propose a framework for DT-based UAV applications in the realm of green IoT, where distinct tasks within the digital twin interact with physical-world UAVs through task manager scheduling. We characterize the radio frequency (RF) attributes of the DT using three-dimensional (3D) millimeter-wave (mmWave) radar imaging on UAVs. The wireless channel modeling, based on ray tracing, underscores the alignment of RF domains between the DT and the physical UAV in a bid to take advantage of multipath reflections and save communication energy. Our numerical findings have justified the efficacy of the drone-enabled DT platform in achieving accurate RF representation of UAVs for the intelligent operation and management of IoT-based green UAV networks. Full article
(This article belongs to the Special Issue Advances of Drones in Green Internet-of-Things)
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