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AI-Enabled Next-Generation Computing and Its 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: 20 August 2025 | Viewed by 2175

Special Issue Editors


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Guest Editor
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, Republic of Korea
Interests: machine learning; data mining; social network analysis; mobile computing
Special Issues, Collections and Topics in MDPI journals
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland
Interests: machine learning (ML); Internet of Things (IoT); ML at the edge; cybersecurity of IoT; massive access

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) continues to advance at an unprecedented pace, its integration into computing systems is driving innovations. The convergence of AI with next-generation computing technologies, such as cloud computing, mobile computing, edge computing, secure and quantum-safe computing, and high-performance computing, is unlocking new frontiers in computational capabilities and enabling a wide range of applications across various domains. This Special Issue aims to provide a comprehensive overview of the current state of the art, identify challenges, and propose solutions for future research directions in AI-enabled next-generation computing. We welcome articles focused on the following relevant topics:

  • Internet of Things (IoT) and Computation of Things;
  • Cloud computing and big data applications;
  • Mobile network computing and multi-access edge computing;
  • Multimedia and communication systems;
  • AI techniques in high-performance computing;
  • Applications of AI in cybersecurity, healthcare, and IoT;
  • Secure and quantum-safe computing;
  • Future mobility applications and ICT convergence technology.

Submissions on other topics that are in accordance with the theme of this Special Issue are also welcome and may take the form of original research articles, reviews, and case studies.

Prof. Dr. Ilsun You
Prof. Dr. Won-Yong Shin
Prof. Dr. Hsing-Chung Chen
Dr. Mert Nakip
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
  • mobile computing
  • edge computing
  • secure and quantum-safe computing
  • high-performance computing
  • next-generation computing
  • IoT

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Published Papers (1 paper)

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Research

24 pages, 1326 KiB  
Article
ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
by Nur Keleşoğlu and Łukasz Sobczak
Appl. Sci. 2024, 14(23), 10844; https://doi.org/10.3390/app142310844 - 23 Nov 2024
Viewed by 820
Abstract
The number of smart homes is increasing steadily. One of the first technologies that comes to mind when talking about smart homes is Zigbee, which stands out for its low cost, low latency, low power consumption, and mesh networking capabilities. One of the [...] Read more.
The number of smart homes is increasing steadily. One of the first technologies that comes to mind when talking about smart homes is Zigbee, which stands out for its low cost, low latency, low power consumption, and mesh networking capabilities. One of the key features of Zigbee is the encryption of payloads within its frames for security purposes. However, being able to decrypt this payload is crucial for fully understanding its operation and for purposes such as testing the network’s security. Therefore, in this paper, we present the decrypted Zigbee IoT Network Traffic dataset, ZigBeeNet. We captured packets using Wireshark in real time from a smart home with 15 Zigbee devices over 20 days and saved them in pcap files. Additionally, we used a key extraction method to obtain the network key, decrypt the payload data, and analyze the characteristic features of network traffic, which we present in this paper. ZigBeeNet will be useful in wider areas than existing datasets with its ability to support network security research, pattern analysis, network performance analysis, and Zigbee traffic generator. We believe that this open-source dataset will contribute significantly to a wide range of industrial and academic research applications. Full article
(This article belongs to the Special Issue AI-Enabled Next-Generation Computing and Its Applications)
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