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Drones
Special Issues
UAV IoT Sensing and Networking
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UAV IoT Sensing and Networking

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 20234

Special Issue Editors

Dr. Mauro Tropea

E-Mail Website
Guest Editor
Department of Computer Engineering, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Rende, Italy
Interests: UAV; satellite; wireless network; multicast communication; QoS; VANET
Prof. Dr. Abderrahmane Lakas

E-Mail Website
Guest Editor
College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates
Interests: computer networks; quality of service; vehicular ad hoc networks; wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Sarigiannidis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research on unmanned aerial vehicles, known also as drones or by the acronym UAVs, has been increasing in recent years. These devices have specific characteristics that make them useful for a multitude of applications ranging from agriculture, emergency and disaster events, coverage issues, healthcare, human and environmental monitoring and many others. The use of UAVs together with IoT devices is fundamental in helping different human activities and facilitating various value-added services. The From joint collaboration of these devices, many integration issues arise that researchers have to study and analyze proposing new and specific solutions at various protocol stack levels. Of interest also is the use of new paradigms such as software-defined networking (SDN) in UAV networks in order to show the new frontiers of research exploiting the advantage of virtualization and artificial intelligence (AI) techniques.

So, the research on UAVs is becoming ever more important and, with this Special Issue, we want to stimulate the scientific community to propose new technical studies that may address different topics such as:

  • UAV and IoT sensing;
  • UAV networking research;
  • Battery and energy issues on UAV and IoT;
  • SDN for UAV and IoT networking;
  • NFV paradigm for UAV and IoT;
  • UAV architectures and platforms;
  • IoT devices for UAV applications;
  • UAV framework and applications;
  • UAV path-planning and trajectory studies;
  • UAV coordination problems;
  • AI and UAV;
  • UAV swarm for complex task;
  • UAV security and privacy;
  • Security aspects on UAV and IoT integration.

Dr. Mauro Tropea
Prof. Dr. Abderrahmane Lakas
Dr. Panagiotis Sarigiannidis
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

  • UAV 
  • IoT 
  • networking 
  • sensing 
  • SDN 
  • energy 
  • security

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

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Editorial

Jump to: Research

4 pages, 150 KiB  
Editorial
UAV IoT Sensing and Networking
by Mauro Tropea, Abderrahmane Lakas and Panagiotis Sarigiannidis
Drones 2024, 8(9), 466; https://doi.org/10.3390/drones8090466 - 7 Sep 2024
Viewed by 723
Abstract
Research on unmanned aerial vehicles, also known as drones or by the acronym UAVs, has been increasing in recent years [...] Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)

Research

Jump to: Editorial

22 pages, 1764 KiB  
Article
Age of Information-Inspired Data Collection and Secure Upload Assisted by the Unmanned Aerial Vehicle and Reconfigurable Intelligent Surface in Maritime Wireless Sensor Networks
by Dawei Wang, Linfeng Yuan, Linna Pang, Qian Xu and Yixin He
Drones 2024, 8(6), 267; https://doi.org/10.3390/drones8060267 - 16 Jun 2024
Cited by 2 | Viewed by 1013
Abstract
This paper proposes an age of information (AoI)-inspired secure transmissions strategy for secure transmission from the maritime wireless sensor network to the onshore base station (BS) with the assistance of the unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), in which eavesdroppers [...] Read more.
This paper proposes an age of information (AoI)-inspired secure transmissions strategy for secure transmission from the maritime wireless sensor network to the onshore base station (BS) with the assistance of the unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), in which eavesdroppers exist near the BS. In the proposed scheme, the secure transmission process is divided into the data collection period and the data upload period according to the time sequence to minimize the age of information (AoI) for the privacy information. In the data collection period, we design two scheduling schemes by selecting the sensor with the smallest current AoI or the largest difference in the adjacent AoI. In addition, we propose two heuristic algorithms by adopting the particle swarm optimization algorithm (PSO) and genetic algorithm (GA) to solve the above two problems. In the data uploading period, the uploading time minimization problem is converted to the secrecy rate maximization problem. We design an iterative optimization algorithm with auxiliary variables that are introduced to optimize the reflection coefficient of the RIS. Simulation results show that the proposed scheme can reduce the average AoI by about 10 s compared to the current methods. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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20 pages, 913 KiB  
Article
Optimization of Full-Duplex UAV Secure Communication with the Aid of RIS
by Huan Lai, Dongfen Li, Fang Xu, Xiao Wang, Jin Ning, Yanmei Hu and Bin Duo
Drones 2023, 7(9), 591; https://doi.org/10.3390/drones7090591 - 20 Sep 2023
Cited by 3 | Viewed by 1624
Abstract
Recently, unmanned aerial vehicles (UAVs) have gained significant popularity and have been extensively utilized in wireless communications. Due to the susceptibility of wireless channels to eavesdropping, interference and other security attacks, UAV communication security faces serious challenges. Therefore, novel solutions need to be [...] Read more.
Recently, unmanned aerial vehicles (UAVs) have gained significant popularity and have been extensively utilized in wireless communications. Due to the susceptibility of wireless channels to eavesdropping, interference and other security attacks, UAV communication security faces serious challenges. Therefore, novel solutions need to be investigated for handling corresponding issues. Note that the UAV with full-duplex (FD) mode can actively improve spectral efficiency, and reconfigurable intelligent surface (RIS) can enable the intelligent control of signal reflection for improving transmission quality. Accordingly, the security of UAV communications may be considerably improved by combining the two techniques mentioned above. In this paper, we investigate the performance of secure communication in urban areas, assisted by a FD UAV and an RIS, where the UAV receives sensitive information from the ground users and sends jamming signals to the ground eavesdroppers. Particularly, we propose an approach to jointly optimize the user scheduling, user transmit power, UAV jamming power, RIS phase shift, and UAV trajectory for maximizing the worst-case secrecy rate. However, the non-convexity of the problem makes it difficult to solve. Combining alternating optimization (AO), slack variable techniques, successive convex approximation (SCA), and semi-definite relaxation (SDR), we propose an effective algorithm to obtain a suboptimal solution. According to the simulation results, in contrast to other benchmark schemes, we show that our proposed algorithm can significantly improve the overall secrecy rate. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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25 pages, 1672 KiB  
Article
Drone-Based Environmental Emergency Response in the Brazilian Amazon
by Janiele Custodio and Hernan Abeledo
Drones 2023, 7(9), 554; https://doi.org/10.3390/drones7090554 - 27 Aug 2023
Viewed by 2206
Abstract
This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation [...] Read more.
This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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22 pages, 631 KiB  
Article
A Dynamic Checkpoint Interval Decision Algorithm for Live Migration-Based Drone-Recovery System
by Bongjae Kim, Jungkyu Han, Joonhyouk Jang, Jinman Jung, Junyoung Heo, Hong Min and Dong Sop Rhee
Drones 2023, 7(5), 286; https://doi.org/10.3390/drones7050286 - 24 Apr 2023
Cited by 2 | Viewed by 2470
Abstract
Numerous services and applications have been developed to monitor anomalies or collect various sensing information in large-scale monitoring areas using drones. Nonetheless, interruptions of drone missions in such areas occasionally occur due to network errors, low battery levels, or physical defects, such as [...] Read more.
Numerous services and applications have been developed to monitor anomalies or collect various sensing information in large-scale monitoring areas using drones. Nonetheless, interruptions of drone missions in such areas occasionally occur due to network errors, low battery levels, or physical defects, such as damage to the rotor and propeller. Checkpointing is a technique that periodically saves the system’s state, allowing it to be restored to that point in the event of a failure. In such circumstances, checkpointing techniques can be used to periodically save information related to the drone mission and replace a malfunctioning drone with the saved checkpoint information. In this paper, we propose a dynamic checkpoint interval decision algorithm for a live migration-based drone-recovery system. The proposed scheme minimizes the drone’s energy consumption while efficiently performing checkpointing. According to the basic experimental results, the proposed scheme consumed only about 3.51% more energy, while performing about 25.97% more checkpoint operations compared to the FIC (Fixed Interval Checkpointing) scheme. By using the proposed scheme, it is possible to increase the availability of checkpoint information and quickly resume drone missions, while minimizing the increase in energy consumption of the drone by saving checkpoints more frequently. Therefore, the proposed scheme can improve the reliability and stability of drone-based services. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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18 pages, 2915 KiB  
Article
Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing
by Fan Li, Juan Luo, Ying Qiao and Yaqun Li
Drones 2023, 7(5), 284; https://doi.org/10.3390/drones7050284 - 22 Apr 2023
Cited by 5 | Viewed by 2232
Abstract
With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial [...] Read more.
With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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42 pages, 4465 KiB  
Article
A Survey on the Design Aspects and Opportunities in Age-Aware UAV-Aided Data Collection for Sensor Networks and Internet of Things Applications
by Oluwatosin Ahmed Amodu, Rosdiadee Nordin, Chedia Jarray, Umar Ali Bukar, Raja Azlina Raja Mahmood and Mohamed Othman
Drones 2023, 7(4), 260; https://doi.org/10.3390/drones7040260 - 11 Apr 2023
Cited by 13 | Viewed by 4226
Abstract
Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things [...] Read more.
Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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21 pages, 6077 KiB  
Article
UAV Charging Station Placement in Opportunistic Networks
by Salih Safa Bacanli, Enas Elgeldawi, Begümhan Turgut and Damla Turgut
Drones 2022, 6(10), 293; https://doi.org/10.3390/drones6100293 - 9 Oct 2022
Cited by 11 | Viewed by 2955
Abstract
Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these [...] Read more.
Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes. Full article
(This article belongs to the Special Issue UAV IoT Sensing and Networking)
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