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Application of Machine Learning and Hybrid Optimization Algorithms in IoT...
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Application of Machine Learning and Hybrid Optimization Algorithms in IoT Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (27 December 2023) | Viewed by 5510

Special Issue Editor

Prof. Dr. He Fang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Soochow City, China
Interests: 5G; communication security and machine learning; intelligent security provision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) systems and their ongoing convergence with diverse industry applications signify the imminent next wave of  ubiquitously connected society. They are expected to support diverse vertical applications by connecting heterogeneous devices, machines, and industrial processes. However, massive devices connected and data generated in IoT systems bring new design challenges and many security vulnerabilities.

When artificial intelligence (AI) merges with the IoT, the existing industrial ecology and economic landscape, and even the pattern of human life can be changed. Explicitly, the abundant information contained in the cloud/system could be utilized for learning and reasoning, thus providing better services based on the trained model and learned knowledge. Moreover, through self-correction by machine learning techniques, real-time intelligent management can be accomplished to adapt to dynamic IoT environments. Furthermore, hybrid optimization algorithms also play an important role in solving joint problems flexibly and effectively in IoT systems.

The overarching aim of this special issue (SI) is to bring together leading researchers in both academia and industry from diverse backgrounds to advance the application of machine learning and hybrid optimization algorithms in IoT. Suitable topics for this SI include, but are not limited to, the following areas:

  • Machine learning algorithms and applications for perception, interaction, and communications in IoT systems;
  • Security and privacy issues as well as their solutions based on machine learning or hybrid optimization algorithms in IoT;
  • Resource allocation designs in IoT based on machine learning techniques or hybrid optimization algorithms;
  • AI-driven data analysis in IoT systems;
  • Knowledge/data/model-based systems for wireless communications;
  • Intelligent network architecture and deployment for IoT.

Prof. Dr. He Fang
Guest Editor

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. Processes 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

  • wireless communications
  • IoT
  • AI
  • machine learning
  • optimization techniques

Published Papers (3 papers)

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Research

19 pages, 1126 KiB  
Article
Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective
by Ao Liu, Shaoshi Yang, Jingsheng Tan, Zongze Liang, Jiasen Sun, Tao Wen and Hongyan Yan
Processes 2023, 11(5), 1560; https://doi.org/10.3390/pr11051560 - 19 May 2023
Viewed by 1363
Abstract
Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there [...] Read more.
Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes. Full article
(This article belongs to the Special Issue Application of Machine Learning and Hybrid Optimization Algorithms in IoT Engineering)
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19 pages, 6482 KiB  
Article
A Deep-Learning Neural Network Approach for Secure Wireless Communication in the Surveillance of Electronic Health Records
by Zhifeng Diao and Fanglei Sun
Processes 2023, 11(5), 1329; https://doi.org/10.3390/pr11051329 - 25 Apr 2023
Cited by 3 | Viewed by 1404
Abstract
The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility [...] Read more.
The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an endorsed joint security scheme (EJSS). This scheme recognizes the EHR utilization based on the aforementioned processes. For each process, different security measures are administered for sustainable security. Access control and storage modification require relative security administered using mutual key sharing between the accessing user and the EHR database. In this process, the learning identifies the variations in different processes for reducing adversarial interruption. The federated learning paradigm employed in this scheme identifies concurrent adversaries in the different processes initiated at the same time. Differentiating the adversaries under each process strengthens mutual authentication using individual attributes. Therefore, individual surveillance efficiency through log inspection and adversary detection is improved for heterogeneous and large-scale EHR databases. Full article
(This article belongs to the Special Issue Application of Machine Learning and Hybrid Optimization Algorithms in IoT Engineering)
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21 pages, 4815 KiB  
Article
An Adaptive Routing Algorithm for Inter-Satellite Networks Based on the Combination of Multipath Transmission and Q-Learning
by Yuanji Shi, Zhiwei Yuan, Xiaorong Zhu and Hongbo Zhu
Processes 2023, 11(1), 167; https://doi.org/10.3390/pr11010167 - 5 Jan 2023
Cited by 2 | Viewed by 2084
Abstract
In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO [...] Read more.
In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO satellite networks, the traditional single shortest path algorithm can no longer meet the optimal path requirement. Therefore, this paper proposes a multi-path routing algorithm based on an improved breadth-first search. First, according to the inter-satellite network topology information, the improved breadth-first search algorithm is used to obtain all the front hop node information of the destination node. Second, all the shortest paths are obtained by backtracking the path through the front hop node. Finally, according to the inter-satellite network, the bandwidth capacity of the traffic and nodes determines the optimal path from multiple shortest paths. However, due to the high dynamics of low-orbit satellite networks, the topology changes rapidly, and the global topology of the network is often not available. At this time, in order to enhance the adaptability of the algorithm, this paper proposes an inter-satellite network dynamic routing algorithm based on reinforcement learning. Verified by simulation experiments, the proposed algorithm can improve the throughput of the inter-satellite network, reduce the time delay, and the packet loss rate. Full article
(This article belongs to the Special Issue Application of Machine Learning and Hybrid Optimization Algorithms in IoT Engineering)
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