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Electronics
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Advances in Intelligent Systems and Networks, 2nd Edition
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Advances in Intelligent Systems and Networks, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1400

Special Issue Editors

Dr. Namgi Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Republic of Korea
Interests: mobile networks; networks protocols; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyunsoo Yoon

E-Mail Website
Guest Editor
School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
Interests: AI information service; secure computing; system networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent systems and networks are advanced technologies that perceive and respond to stimuli. Intelligent systems and networks can take many forms and continually communicate and interact with their environments. The study of how systems understand and process information emerged in the 1960s and has since grown into an important technology that is central to industries, society, and academia. Network technology has also exponentially improved due to the tremendous success of Internet services. This Special Issue focuses on advances in intelligent systems and networks. The potential topics include, but are not limited to, intelligent systems and networks for:

  • Advanced networks;
  • Big data systems;
  • Cognitive systems;
  • Computational intelligence;
  • Intelligent pattern recognition
  • Intelligent systems;
  • Intelligent control;
  • Intelligent IoT and IIoT;
  • Intelligent image processing;
  • Machine learning;
  • Multimedia systems.

Dr. Namgi Kim
Prof. Dr. Hyunsoo Yoon
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. Electronics 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

  • advanced networks
  • artificial intelligence
  • big data
  • computational intelligence
  • image processing
  • intelligent systems
  • IoT
  • multimedia systems

Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 5886 KiB  
Article
Civil Aeronautical Ad Hoc Network Zero-Overhead Clustering Algorithm Based on Realtime Position Information of the Aircraft
by Changyuan Luo, Lianxiang Li, Duan Li, Peisen Liu and Muhammad Saad Khan
Electronics 2024, 13(1), 232; https://doi.org/10.3390/electronics13010232 - 04 Jan 2024
Viewed by 530
Abstract
Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according [...] Read more.
Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according to the real-time position of the aircraft based on the known trajectory. Firstly, the route and trajectory models are used to obtain geographical coordinates by the aircraft positioning algorithm. On this basis, the geographical cluster and cluster head region are divided in order to complete the cluster setting. Considering the aircraft maintenance cluster generation time updates, we use the communication sub-cluster generation algorithm to control the size of the cluster, and also, the flexibility of cluster hops is guaranteed by the subsidiary cluster members. The continuity of communication and the scalability of the cluster are maintained by the gateway node, thereby forming a network structure and increasing the stability of clusters. Finally, the actual route data are used to simulate the performance of the algorithm. The experimental and analytical results show that clustering and maintenance of the algorithm have zero overhead. Additionally, compared with the traditional algorithm, our proposed method can maintain a reasonable number of clusters, reduce the frequency of cluster head replacement, reduce the number of cluster members entering and leaving the cluster and avoid the loss of control of cluster heads to cluster members. So, it has important application value in the field of civil aviation. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks, 2nd Edition)
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23 pages, 5639 KiB  
Article
Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning
by Yangzhou Xu, Jia Lou, Tiantian Wang, Junxiao Shi, Tao Zhang, Agyemang Paul and Zhefu Wu
Electronics 2023, 12(23), 4845; https://doi.org/10.3390/electronics12234845 - 30 Nov 2023
Viewed by 665
Abstract
In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This [...] Read more.
In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks, 2nd Edition)
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