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Applied Sciences
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Edge-Enabled Big Data Intelligence for B5G and IoT Applications
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Edge-Enabled Big Data Intelligence for B5G and IoT Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 6296

Special Issue Editors

Prof. Dr. Chuan-Ming Liu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 10608, Taiwan
Interests: big data management and processing; uncertain data management; data science; data management over edge computing; spatial data processing; data streams; ad hoc and sensor networks; location-based services
Special Issues, Collections and Topics in MDPI journals
Dr. Chuan-Chi Lai

E-Mail Website
Guest Editor
Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: UAV networks; edge intelligence; IoT data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) plays a key role in the realization of smart cities, healthcare, and Industry 4.0. Massive implementations of the IoT continue to accumulate massive amounts of data. Big data processing and analysis technologies are playing an increasingly important role, making the world simpler, better, and smarter. The intelligence extracted from data processing and analysis especially provides unprecedented opportunities for a new wave of emerging applications. Meanwhile, it is well-known that big data processing and analysis require substantial computing and storage resources, which can be offered through cloud computing much easier than traditional IT. On the other hand, due to the needs of emerging 5G and B5G applications, there are many requirements in processing field data analysis to achieve low-latency response, such as unmanned driving and the intelligent control of manufacturing and transportation , which can be enabled through cloud/fog/edge computing. Therefore, we see the huge potential of cloud/fog/edge-based big data intelligence. This Special Issue aims to cover various aspects related to big data processing and analysis in conjunction with cloud/Fog/Edge computing for IoT, including, but not limited to, the following topics:

Prof. Dr. Chuan-Ming Liu
Dr. Chuan-Chi Lai
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • cloud computing, fog computing, and edge computing in B5G and IoT
  • novel theories, concepts, and paradigms of the convergence of AI, IoT, and Edge–Cloud
  • Artificial Intelligence, machine learning, and data science in/for Edge–Cloud–IoT
  • distributed computing architectures, algorithms, and models in B5G and IoT
  • IoT data analytics models, algorithms, and applications
  • edge-enabled big data intelligence in blockchain IoT
  • explainable AI for IoT data processing
  • big data intelligence for IoT security (authentication, access control, security models), privacy preservation, and data protection
  • information integrity and fusion in IoT
  • big data intelligence for IoT communications and networking
  • multi-objective decision making/optimization in B5G and IoT applications
  • application and case studies (healthcare, Industry 4.0, energy, smart city, finance, etc.)

Published Papers (2 papers)

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Research

14 pages, 10331 KiB  
Article
Effective Remote Sensing from the Internet of Drones through Flying Control with Lightweight Multitask Learning
by Chao-Yang Lee, Huan-Jung Lin, Ming-Yuan Yeh and Jer Ling
Appl. Sci. 2022, 12(9), 4657; https://doi.org/10.3390/app12094657 - 6 May 2022
Cited by 4 | Viewed by 1846
Abstract
The rapid development and availability of drones has raised growing interest in their numerous applications, especially for aerial remote-sensing tasks using the Internet of Drones (IoD) for smart city applications. Drones image a large-scale, high-resolution, and no visible band short wavelength infrared (SWIR) [...] Read more.
The rapid development and availability of drones has raised growing interest in their numerous applications, especially for aerial remote-sensing tasks using the Internet of Drones (IoD) for smart city applications. Drones image a large-scale, high-resolution, and no visible band short wavelength infrared (SWIR) ground aerial map of the investigated area for remote sensing. However, due to the high-altitude environment, a drone can easily jitter due to dynamic weather conditions, resulting in blurred SWIR images. Furthermore, it can easily be influenced by clouds and shadow images, thereby resulting in the failed construction of a remote-sensing map. Most UAV remote-sensing studies use RGB cameras. In this study, we developed a platform for intelligent aerial remote sensing using SWIR cameras in an IoD environment. First, we developed a prototype for an aerial SWIR image remote-sensing system. Then, to address the low-quality aerial image issue and reroute the trajectory, we proposed an effective lightweight multitask deep learning-based flying model (LMFM). The experimental results demonstrate that our proposed intelligent drone-based remote-sensing system efficiently stabilizes the drone using our designed LMFM approach in the onboard computer and successfully builds a high-quality aerial remote-sensing map. Furthermore, the proposed LMFM has computationally efficient characteristics that offer near state-of-the-art accuracy at up to 6.97 FPS, making it suitable for low-cost low-power devices. Full article
(This article belongs to the Special Issue Edge-Enabled Big Data Intelligence for B5G and IoT Applications)
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16 pages, 3240 KiB  
Article
COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction
by Sardar Khaliq uz Zaman, Ali Imran Jehangiri, Tahir Maqsood, Arif Iqbal Umar, Muhammad Amir Khan, Noor Zaman Jhanjhi, Mohammad Shorfuzzaman and Mehedi Masud
Appl. Sci. 2022, 12(7), 3312; https://doi.org/10.3390/app12073312 - 24 Mar 2022
Cited by 26 | Viewed by 2800
Abstract
In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station [...] Read more.
In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced. Full article
(This article belongs to the Special Issue Edge-Enabled Big Data Intelligence for B5G and IoT Applications)
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