Edge Computing and IoT Technologies for Drones

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 11400

Special Issue Editors

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-0058, Japan
Interests: embedded and cyber-physical systems; electronic design automation and optimization; autonomous drones; biochip synthesis
Special Issues, Collections and Topics in MDPI journals
Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Interests: image/video processing for embedded system; design methodology for embedded systems
Special Issues, Collections and Topics in MDPI journals
Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-0058, Japan
Interests: embedded systems; image processing in healthcare; IoT systems
Special Issues, Collections and Topics in MDPI journals
Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Interests: embedded systems; task scheduling; high-level synthesis; approximate computing

Special Issue Information

Dear Colleagues,

Edge computing systems embedded in drones play an extremely important role in their autonomous flights. Edge computing systems detect obstacles, such as camera and radar images, determine a flight path to avoid obstacles and reach the required destination, and control propellers in order to maintain a flight path. These processes require an enormous amount of computation, but due to some constraints, for example batteries, it is not practical to employ high-performance processors. Offloading heavy computation such as AI-based object detection to cloud servers through IoT technology is also effective, but safe flights must continue even if the connection becomes unstable or is lost.

This Special Issue aims to publish the latest research and developments in edge computing and IoT technologies for drones. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Embedded and edge computing systems for drones;
  • IoT technologies for drones;
  • Machine learning for drones;
  • Computer vision for drones;
  • AR, VR and metaverse for drones;
  • Drone flight simulation;
  • Path planning and routing of drones;
  • Energy-efficient computing for drones;
  • Reliable and secure computing for drones.

This Special Issue is an open invitation but we also invite selected papers from the International Symposium on Advanced Technologies and Applications in the Internet of Things (ATAIT) 2022.

We look forward to receiving your contributions.

Prof. Dr. Hiroyuki Tomiyama
Dr. Ittetsu Taniguchi
Dr. Xiangbo Kong
Dr. Hiroki Nishikawa
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.

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 2479 KiB  
Article
Implementation of an Edge-Computing Vision System on Reduced-Board Computers Embedded in UAVs for Intelligent Traffic Management
Drones 2023, 7(11), 682; https://doi.org/10.3390/drones7110682 - 20 Nov 2023
Cited by 1 | Viewed by 1557
Abstract
Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). [...] Read more.
Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). UAVs, owing to their aerial perspective, offer a more effective means of detecting vehicles involved in these maneuvers. The primary objective is to develop, evaluate, and compare different computer vision models and reduced-board (and small-power) hardware for optimizing traffic management in these scenarios. A dataset was constructed using two sources, several models (YOLO 5 and 8, DETR, and EfficientDetLite) were selected and trained, four reduced-board computers were chosen (Raspberry Pi 3B+ and 4, Jetson Nano, and Google Coral), and the models were tested on these boards for edge computing in UAVs. The experiments considered training times (with the dataset and its optimized version), model metrics were obtained, inference frames per second (FPS) were measured, and energy consumption was quantified. After the experiments, it was observed that the combination that best suits our use case is the YoloV8 model with the Jetson Nano. On the other hand, a combination with much higher inference speed but lower accuracy involves the EfficientDetLite models with the Google Coral board. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

31 pages, 1146 KiB  
Article
DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks
Drones 2023, 7(11), 656; https://doi.org/10.3390/drones7110656 - 01 Nov 2023
Viewed by 1412
Abstract
With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort [...] Read more.
With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

23 pages, 5616 KiB  
Article
Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones
Drones 2023, 7(10), 622; https://doi.org/10.3390/drones7100622 - 05 Oct 2023
Cited by 1 | Viewed by 1653
Abstract
The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It [...] Read more.
The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

20 pages, 1263 KiB  
Article
Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing
Drones 2023, 7(7), 443; https://doi.org/10.3390/drones7070443 - 05 Jul 2023
Cited by 2 | Viewed by 950
Abstract
Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as [...] Read more.
Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0–1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

22 pages, 676 KiB  
Article
A Computation Offloading Scheme for UAV-Edge Cloud Computing Environments Considering Energy Consumption Fairness
Drones 2023, 7(2), 139; https://doi.org/10.3390/drones7020139 - 16 Feb 2023
Cited by 4 | Viewed by 2027
Abstract
A heterogeneous computing environment has been widely used with UAVs, edge servers, and cloud servers operating in tandem. Various applications can be allocated and linked to the computing nodes that constitute this heterogeneous computing environment. Efficiently offloading and allocating computational tasks is essential, [...] Read more.
A heterogeneous computing environment has been widely used with UAVs, edge servers, and cloud servers operating in tandem. Various applications can be allocated and linked to the computing nodes that constitute this heterogeneous computing environment. Efficiently offloading and allocating computational tasks is essential, especially in these heterogeneous computing environments with differentials in processing power, network bandwidth, and latency. In particular, UAVs, such as drones, operate using minimal battery power. Therefore, energy consumption must be considered when offloading and allocating computational tasks. This study proposed an energy consumption fairness-aware computational offloading scheme based on a genetic algorithm (GA). The proposed method minimized the differences in energy consumption by allocating and offloading tasks evenly among drones. Based on performance evaluations, our scheme improved the efficiency of energy consumption fairness, as compared to previous approaches, such as Liu et al.’s scheme. We showed that energy consumption fairness was improved by up to 120%. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

17 pages, 15087 KiB  
Article
Fast and High-Quality Monocular Depth Estimation with Optical Flow for Autonomous Drones
Drones 2023, 7(2), 134; https://doi.org/10.3390/drones7020134 - 14 Feb 2023
Cited by 2 | Viewed by 2513
Abstract
Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based [...] Read more.
Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications. Full article
(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)
Show Figures

Figure 1

Back to TopTop