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Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao

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A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 16416

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Special Issue Editors

Prof. Dr. Kuo-Hui Yeh

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Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
Interests: IoT security; blockchain; mobile security; NFC/RFID security; authentication; data privacy; cryptology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chien-Ming Chen

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College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
Interests: network security; blockchain; big data; AI; data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Wei-Che Chien

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Guest Editor

Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien 97401, Taiwan
Interests: wireless rechargeable sensor networks; mobile networks; AIoT

Special Issue Information

Dear Colleagues,

Han-Chieh Chao received his MS and PhD degrees in Electrical Engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1993, respectively. He is currently a professor at the Department of Electrical Engineering, National Dong Hwa University, where he also serves as president. He is also works with the Department of Computer Science and Information Engineering, National Ilan University, Taiwan. He was the Director of the Computer Center for Ministry of Education Taiwan from September 2008 to July 2010. His research interests include IPv6, cross-layer design, cloud computing, IoT, and 5G mobile networks. He has authored or co-authored four books and has published about 400 refereed professional research papers. He has supervised more than 150 MSEE thesis students and 11 PhD students. Dr. Chao has been ranked as the top 10 Computer Scientists in Taiwan for 2020 by Guide2Research. Dr. Chao was ranked as a Top 5 Taiwan Author in the field of Computer Networks & Communications, Information Systems, Computer Science Applications, and Hardware & Architecture by Scopus SciVal. Due to Dr. Chao’s contribution to suburban ICT education, he has been awarded the US President's Lifetime Achievement Award and International Albert Schweitzer Foundation Human Contribution Award in 2016.

Prof. Chao has undertaken a pioneering role in the development and promotion of practical solutions for NGN. His research accomplishments are fundamental, innovative, and insightful, which were recognized by both academia and industry. The OSI-layered protocol works well in wired networks, but the traditional layered network design cannot meet the needs of users in terms of performance and efficiency for NGN. Prof. Chao proposed "Cross-Layer Design" for 3G and 4G, which abandons the restriction that the layered architecture does not allow direct communication between adjacent layers, and can transmit or share information between different protocol layers according to system requirements, thus reducing the complexity of network planning and enhancing the flexibility of the network. The technology has also become one of the major development technologies of 5G. In this regard, he proposed novel CLD frameworks (including the integration of SDN and SDR, and STIN), and conducted systematic studies to comprehensively evaluate system performance regarding delay, traffic load, cost, and quality of service (QoS). These studies transcend the wireless access, networking, transport, and application layers, and clearly show that the proposed framework can effectively increase system capability, affordability, and sustainability.

Prof. Chao first proposed and established a novel network framework based on cross-layer design technology for the Taiwan Academic Network (TANet). He cooperated with Cisco Taiwan to build the first commercial grade 5G evolved packet core (EPC) testbed in Taiwan’s academia. This project, along with his research results, strengthened cooperation with Taiwan’s leading telecommunication company FET (Fareaastone) and Ericsson, to further develop a 5G testing on campus and set up the 5G lab in Taiwan to accelerate industrial and social transformation in the age of NGN and IoT. FET starts to offer 5G services on its LTE technology in 2018, 2 years ahead of commercial operations. In addition, the proposed STIN vision is included in the conclusion of 3GPP standard TR 38.811, as one of the working items of R16.

It is with this in mind that we honor Prof. Chao on this occasion, for his immense contributions in the areas of cross-layer design, cloud computing, IoT, and mobile networks. His excellent research results are an inspiration to all, and we are pleased to invite you to submit an article to this Special Issue of the journal Symmetry.

Symmetry is an extraordinary characteristic which has widely been deployed in different research fields of computer engineering, such as symmetric architecture for telecommunications, symmetric network structures, and symmetric algorithms. This special issue welcomes studies involving the concept of Symmetry, potential research domains include but not limited to:

• 5G/B5G/6G
• Deep learning
• Mobile networks
• Cross-layer design
• Wireless sensor networks
• Cloud computing
• Edge computing
• Internet of Things
• Software-defined networks Security and privacy

Prof. Dr. Kuo-Hui Yeh
Prof. Dr. Chien-Ming Chen
Dr. Wei-Che Chien
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. Symmetry 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 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

5G/B5G/6G
Deep learning
Mobile networks
Cross-layer design
Wireless sensor networks
Cloud computing
Edge computing
Internet of Things
Software-defined networks Security and privacy

Published Papers (7 papers)

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Editorial

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4 pages, 165 KiB

Open AccessEditorial

Special Issue Editorial “Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao”

by Kuo-Hui Yeh, Chien-Ming Chen and Wei-Che Chien

Symmetry 2023, 15(4), 882; https://doi.org/10.3390/sym15040882 - 08 Apr 2023

Viewed by 855

Abstract

Han-Chieh Chao received his MS and PhD degrees in Electrical Engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1993, respectively [...] Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

Research

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17 pages, 453 KiB

Open AccessArticle

A Survey on Resource Management for Cloud Native Mobile Computing: Opportunities and Challenges

by Shih-Yun Huang, Cheng-Yu Chen, Jen-Yeu Chen and Han-Chieh Chao

Symmetry 2023, 15(2), 538; https://doi.org/10.3390/sym15020538 - 17 Feb 2023

Cited by 7 | Viewed by 3423

Abstract

Fifth-generation mobile communication networks (5G)/Beyond 5G (B5G) can achieve higher data rates, more significant connectivity, and lower latency to provide various mobile computing service categories, of which enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low latency communications (URLLC) are the three extreme cases. A symmetrically balanced mechanism must be considered in advance to fit the different requirements of such a wide variety of service categories and ensure that the limited resource capacity has been properly allocated. Therefore, a new network service architecture with higher flexibility, dispatchability, and symmetrical adaptivity is demanded. The cloud native architecture that enables service providers to build and run scalable applications/services is highly favored in such a setting, while a symmetrical resource allocation is still preserved. The microservice function in the cloud native architecture can further accelerate the development of various services in a 5G/B5G mobile wireless network. In addition, each microservice part can handle a dedicated service, making overall network management easier. There have been many research and development efforts in the recent literature on topics pertinent to cloud native, such as containerized provisioning, network slicing, and automation. However, there are still some problems and challenges ahead to be addressed. Among them, optimizing resource management for the best performance is fundamentally crucial given the challenge that the resource distribution in the cloud native architecture may need more symmetry. Thus, this paper will survey cloud native mobile computing, focusing on resource management issues of network slicing and containerization. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

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26 pages, 1748 KiB

Open AccessArticle

A Genetic Algorithm for the Waitable Time-Varying Multi-Depot Green Vehicle Routing Problem

by Chien-Ming Chen, Shi Lv, Jirsen Ning and Jimmy Ming-Tai Wu

Symmetry 2023, 15(1), 124; https://doi.org/10.3390/sym15010124 - 01 Jan 2023

Cited by 36 | Viewed by 2087

Abstract

In an era where people in the world are concerned about environmental issues, companies must reduce distribution costs while minimizing the pollution generated during the distribution process. For today’s multi-depot problem, a mixed-integer programming model is proposed in this paper to minimize all costs incurred in the entire transportation process, considering the impact of time-varying speed, loading, and waiting time on costs. Time is directional; hence, the problems considered in this study are modeled based on asymmetry, making the problem-solving more complex. This paper proposes a genetic algorithm combined with simulated annealing to solve this issue, with the inner and outer layers solving for the optimal waiting time and path planning problem, respectively. The mutation operator is replaced in the outer layer by a neighbor search approach using a solution acceptance mechanism similar to simulated annealing to avoid a local optimum solution. This study extends the path distribution problem (vehicle-routing problem) and provides an alternative approach for solving time-varying networks. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

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19 pages, 3679 KiB

Open AccessArticle

A Secure Interoperability Management Scheme for Cross-Blockchain Transactions

by Kuo-Hui Yeh, Guan-Yan Yang, Chanapha Butpheng, Lin-Fa Lee and Ying-Ho Liu

Symmetry 2022, 14(12), 2473; https://doi.org/10.3390/sym14122473 - 22 Nov 2022

Cited by 6 | Viewed by 1964

Abstract

Blockchain technology has recently attracted tremendous interest due to its potential to revolutionize the industry by achieving decentralization while increasing the number of data sources, transparency, reliability, auditability, and trustworthiness. However, one of the major barriers to the widespread adoption of blockchain applications is the lack of mutual consensus and management across blockchains. Cross-blockchain consensus refers to one blockchain network reaching a consensus with another blockchain network to provide the ability to interact and share data. In this paper, we propose a secure management scheme with symmetric cross-blockchain communication and certificateless signature primitives, in which two heterogeneous blockchains are linked by a relay chain to simultaneously deliver cross-blockchain transaction security, achieve compatibility among various blockchains, and ensure the consistency of data exchanged, in practice. Additionally, our evaluation and security analysis shows the practicability and security of our proposed management scheme and demonstrates that a common test platform based on Ethereum can achieve acceptable computation costs. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

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15 pages, 250 KiB

Open AccessArticle

Research and Application of Improved Clustering Algorithm in Retail Customer Classification

by Chu Fang and Haiming Liu

Symmetry 2021, 13(10), 1789; https://doi.org/10.3390/sym13101789 - 26 Sep 2021

Cited by 14 | Viewed by 4457

Abstract

Clustering is a major field in data mining, which is also an important method of data partition or grouping. Clustering has now been applied in various ways to commerce, market analysis, biology, web classification, and so on. Clustering algorithms include the partitioning method, hierarchical clustering as well as density-based, grid-based, model-based, and fuzzy clustering. The K-means algorithm is one of the essential clustering algorithms. It is a kind of clustering algorithm based on the partitioning method. This study’s aim was to improve the algorithm based on research, while with regard to its application, the aim was to use the algorithm for customer segmentation. Customer segmentation is an essential element in the enterprise’s utilization of CRM. The first part of the paper presents an elaboration of the object of study, its background as well as the goal this article would like to achieve; it also discusses the research the mentality and the overall content. The second part mainly introduces the basic knowledge on clustering and methods for clustering analysis based on the assessment of different algorithms, while identifying its advantages and disadvantages through the comparison of those algorithms. The third part introduces the application of the algorithm, as the study applies clustering technology to customer segmentation. First, the customer value system is built through AHP; customer value is then quantified, and customers are divided into different classifications using clustering technology. The efficient CRM can thus be used according to the different customer classifications. Currently, there are some systems used to evaluate customer value, but none of them can be put into practice efficiently. In order to solve this problem, the concept of continuous symmetry is introduced. It is very important to detect the continuous symmetry of a given problem. It allows for the detection of an observable state whose components are nonlinear functions of the original unobservable state. Thus, we built an evaluating system for customer value, which is in line with the development of the enterprise, using the method of data mining, based on the practical situation of the enterprise and through a series of practical evaluating indexes for customer value. The evaluating system can be used to quantify customer value, to segment the customers, and to build a decision-supporting system for customer value management. The fourth part presents the cure, mainly an analysis of the typical k-means algorithm; this paper proposes two algorithms to improve the k-means algorithm. Improved algorithm A can get the K automatically and can ensure the achievement of the global optimum value to some degree. Improved Algorithm B, which combines the sample technology and the arrangement agglomeration algorithm, is much more efficient than the k-means algorithm. In conclusion, the main findings of the study and further research directions are presented. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

24 pages, 11499 KiB

Open AccessArticle

Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks

by Lingxia Liao, Victor C. M. Leung, Zhi Li and Han-Chieh Chao

Symmetry 2021, 13(7), 1133; https://doi.org/10.3390/sym13071133 - 24 Jun 2021

Cited by 13 | Viewed by 2038

Abstract

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

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Review

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27 pages, 3030 KiB

Open AccessReview

Applying Federated Learning in Software-Defined Networks: A Survey

by Xiaohang Ma, Lingxia Liao, Zhi Li, Roy Xiaorong Lai and Miao Zhang

Symmetry 2022, 14(2), 195; https://doi.org/10.3390/sym14020195 - 20 Jan 2022

Cited by 17 | Viewed by 4560

Abstract

Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs. Full article

(This article belongs to the Special Issue Deep Learning Technologies for Mobile Networks: A Themed Issue in Honor of Prof. Han-Chieh Chao)

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