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Drones
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Drone Computing Enabling IoE
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Drone Computing Enabling IoE

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 28103

Special Issue Editors

Dr. Saeed Hamood Alsamhi

E-Mail Website
Guest Editor
Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
Interests: energy efficiency; green and smart environments; drones edge intelligence; industry 4.0; green IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Faris A. Almalki

E-Mail Website
Guest Editor
Computer Engineering Department, College of Computers & IT, Taif University, Taif 26571, Kingdom of Saudi Arabia
Interests: drones; satellites; low- and high-altitude platforms and their applications in ad hoc wireless networks
Dr. Jahan Hassan

E-Mail Website
Guest Editor
School of Engineering and Technology, The Central Queensland University, Sydney, NSW 2000, Australia
Interests: service provisioning using civilian drones; drone energy issues; drone energy harvesting; drone-assisted IoT
Dr. Sudheesh Puthenveettil Gopi

E-Mail Website
Guest Editor
Department of Electronics & Communication Engineering, Manipal Institute of Technology, Manipal 576104, India
Interests: machine learning in wireless communication; joint optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Alexey V. Shvetsov

E-Mail Website
Guest Editor
Department of Operation of Road Transport and Car Service, North-Eastern Federal University, 677000 Yakutsk, Russia
Interests: aerospace engineering; safety engineering; unmanned aerial vehicles; transportation system
Dr. Deepak GC

E-Mail Website
Guest Editor
Department of Networks and Digital Media, Kingston University London, Kingston upon Thames, Surrey KT1 2EE, UK
Interests: cyber security; digital forensics; IoT; physical layer security; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In future sixth-generation (6G) networks, drone-based aerial access networks have been identified as significant enablers of different Internet of Everything (IoE) applications and services. Drone edge computing can serve better computing with low latency due to its capability to move closer to smart environments and gather data effectively and efficiently.  For instance, multiple drones may be deployed to gather data from smart environments and analyse data collaboratively. Machine learning can be used in drones to improve the delivery of smart services to users, people and smart devices, using terrestrial communication infrastructure to improve operational performance. Drone computing for supporting IoE is still in the early stage; therefore, much more effort should be made to improve drone computing applications in 6G networks. Theoretical experimental and frameworks are of high relevance to the Special Issue.

This Special Issue aims to publish the latest contributions in the development of methods and mechanisms for drone computing enabling IoE. Researchers, developers, and industry practitioners working in this area are invited to present their views on the current trends, challenges, and state-of-the-art solutions addressing various issues in drone computing enabling the Internet of Everything. Review papers on the topic are also welcome. Some relevant topics include, but are not limited to, the following:

  • Drone computing application and benefits;
  • Drone computing architecture for IoE scenarios;
  • Energy efficiency of drone computing over smart environments;
  • Privacy and security of drone computing;
  • Drone computing enabling IoE in 6G networks;
  • ML-empowered drones computing for smart environments;
  • Federated learning for improving the decentralized collaboration of drone computing;
  • Blockchain technology for securing drone computing collaborations for smart environments;
  • Collaboration of multi-drone edge computing over smart environments;
  • Multi-drone collaboration.

Dr. Saeed Hamood Alsamhi
Dr. Faris A. Almalki
Dr. Jahan Hassan
Dr. Sudheesh Puthenveettil Gopi
Dr. Alexey V. Shvetsov
Dr. Deepak GC
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.

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Published Papers (4 papers)

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Research

26 pages, 4053 KiB  
Article
DEDG: Cluster-Based Delay and Energy-Aware Data Gathering in 3D-UWSN with Optimal Movement of Multi-AUV
by Reem Alkanhel, Amir Chaaf, Nagwan Abdel Samee, Manal Abdullah Alohali, Mohammed Saleh Ali Muthanna, Dmitry Poluektov and Ammar Muthanna
Drones 2022, 6(10), 283; https://doi.org/10.3390/drones6100283 - 1 Oct 2022
Cited by 8 | Viewed by 2594
Abstract
The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. [...] Read more.
The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. The consumption of energy and delays are minimized by means of an autonomous unmanned vehicle (AUV). This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D-UWSN. Energy in the sensor nodes is saved by clustering and scheduling; on the other hand, the delay is minimized by the movement of the AUV and inter-cluster routing. In clustering, multi-objective spotted hyena optimization (MO-SHO) is applied for the selection of the best sensor for the cluster head, which is responsible for assigning sleep schedules for members. According to the total number of members, an equal half of the members is provided with sleep slots based on the energy and hop counts. The redundancy in the gathered data is eliminated by measuring the Hassanat distance. Then, the moving AUV is able to predict its movement by the di-factor actor–critic path prediction method. The mid-point among the four heads is determined so that the AUV can collect data from four heads at a time. In cases where the waiting time of the CH is exceeded, three-step, inter-cluster routing is executed. The three steps are the discovery of possible routes, ignoring the longest paths and validating the filtered path with a fuzzy–LeNet method. In this 3D-UWSN, the sensed data are not always normal, and, hence, a weighted method is presented to transfer emergency events by selecting forwarders. This work is implemented on Network Simulator version 3.26 to test the results. It achieves better efficiency in terms of data collection delay, end-to-end delay, AUV tour length, network lifetime, number of alive nodes and energy consumption. Full article
(This article belongs to the Special Issue Drone Computing Enabling IoE)
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29 pages, 1222 KiB  
Article
Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0
by Saeed Hamood Alsamhi, Alexey V. Shvetsov, Santosh Kumar, Jahan Hassan, Mohammed A. Alhartomi, Svetlana V. Shvetsova, Radhya Sahal and Ammar Hawbani
Drones 2022, 6(7), 177; https://doi.org/10.3390/drones6070177 - 18 Jul 2022
Cited by 75 | Viewed by 8792
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of [...] Read more.
Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research. Full article
(This article belongs to the Special Issue Drone Computing Enabling IoE)
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21 pages, 13127 KiB  
Article
UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation
by Saeed Hamood Alsamhi, Alexey V. Shvetsov, Santosh Kumar, Svetlana V. Shvetsova, Mohammed A. Alhartomi, Ammar Hawbani, Navin Singh Rajput, Sumit Srivastava, Abdu Saif and Vincent Omollo Nyangaresi
Drones 2022, 6(7), 154; https://doi.org/10.3390/drones6070154 - 22 Jun 2022
Cited by 99 | Viewed by 10269
Abstract
Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating [...] Read more.
Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments. Full article
(This article belongs to the Special Issue Drone Computing Enabling IoE)
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23 pages, 3515 KiB  
Article
Deep Learning-Based Energy Optimization for Edge Device in UAV-Aided Communications
by Chengbin Chen, Jin Xiang, Zhuoya Ye, Wanyi Yan, Suiling Wang, Zhensheng Wang, Pingping Chen and Min Xiao
Drones 2022, 6(6), 139; https://doi.org/10.3390/drones6060139 - 3 Jun 2022
Cited by 12 | Viewed by 3615
Abstract
Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically [...] Read more.
Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively. Full article
(This article belongs to the Special Issue Drone Computing Enabling IoE)
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