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Mathematical Models for Emerging Internet of Things: Communications, Computing and...
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Mathematical Models for Emerging Internet of Things: Communications, Computing and Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3104

Special Issue Editors

Dr. Bin Jiang

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Interests: internet of things; communications and security; intelligent perception
Special Issues, Collections and Topics in MDPI journals
Dr. Yongxin Liu

E-Mail Website
Guest Editor
Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Interests: artificial intelligence; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Tennessee at Martin, Martin, TN 38238, USA
Interests: machine learning; unmanned aerial vehicles; wireless networking
Special Issues, Collections and Topics in MDPI journals
Dr. Shuteng Niu

E-Mail Website
Guest Editor
Department of Computer Science, Bowling Green State University, Ohio, AL 36117, USA
Interests: machine learning; transfer learning; biomedical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous progress of intelligent sensing, efficient communications, high-performance computing, and reliable security, the Internet of Things has ushered in a revolutionary development. In the last decade, complex mathematical models have made great contributions, offering a firm and stable theoretical foundation in IoT technologies. Mathematics-based intelligent perception and sensing help devices to obtain more comprehensive environmental information. Convex optimization and statistical models are widely used in IoT communications. Graph database computing and neural networks achieve the requested prediction and decision in IoT systems. In addition, data encryption and differential computing can play a crucial role in protecting the privacy of the IoT. Against this background, mathematical models for emerging IoT are drawing increasing attention from both researchers and industry. Generally, we consider the effects in three parts: communications, computing, and security.

This Special Issue aims to collect new, innovative ideas to apply mathematical models in emerging IoT systems. Both primarily theoretical deduction and applied IoT technologies based on mathematical ideas are welcomed in this Special Issue. In addition, we especially call for papers that explore and combine both basic theory and original applications of mathematics in IoT systems.       

Dr. Bin Jiang
Dr. Yongxin Liu
Dr. Jian Wang
Dr. Shuteng Niu
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. Mathematics 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 2600 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

  • mathematics-driven IoT dynamical systems
  • convex optimization in IoT communications
  • IoT data transmission via statistical model
  • mathematics-driven IoT security
  • IoT data encryption and privacy protection
  • mathematics-driven machine learning for IoT
  • general and geometric topology for IoT
  • mathematics-driven intelligent sensing in IoT
  • mathematical model in IoT bionic perception

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Published Papers (2 papers)

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Research

28 pages, 2121 KiB  
Article
Deriving Exact Mathematical Models of Malware Based on Random Propagation
by Rodrigo Matos Carnier, Yue Li, Yasutaka Fujimoto and Junji Shikata
Mathematics 2024, 12(6), 835; https://doi.org/10.3390/math12060835 - 12 Mar 2024
Cited by 1 | Viewed by 1094
Abstract
The advent of the Internet of Things brought a new age of interconnected device functionality, ranging from personal devices and smart houses to industrial control systems. However, increased security risks have emerged in its wake, in particular self-replicating malware that exploits weak device [...] Read more.
The advent of the Internet of Things brought a new age of interconnected device functionality, ranging from personal devices and smart houses to industrial control systems. However, increased security risks have emerged in its wake, in particular self-replicating malware that exploits weak device security. Studies modeling malware epidemics aim to predict malware behavior in essential ways, usually assuming a number of simplifications, but they invariably simplify the single most important subdynamics of malware: random propagation. In our previous work, we derived and presented the first exact mathematical model of random propagation, defined as the subdynamics of propagation of a malware model. The propagation dynamics were derived for the SIS model in discrete form. In this work, we generalize the methodology of derivation and extend it to any Markov chain model of malware based on random propagation. We also propose a second method of derivation based on modifying the simplest form of the model and adjusting it for more complex models. We validated the two methodologies on three malware models, using simulations to confirm the exactness of the propagation dynamics. Stochastic errors of less than 0.2% were found in all simulations. In comparison, the standard nonlinear model of propagation (present in ∼95% of studies) has an average error of 5% and a maximum of 9.88% against simulations. Moreover, our model has a low mathematical trade-off of only two additional operations, being a proper substitute to the standard literature model whenever the dynamical equations are solved numerically. Full article
(This article belongs to the Special Issue Mathematical Models for Emerging Internet of Things: Communications, Computing and Security)
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18 pages, 526 KiB  
Article
Blockwise Joint Detection of Physical Cell Identity and Carrier Frequency Offset for Narrowband IoT Applications
by Young-Hwan You, Yong-An Jung, Sung-Hun Lee and Intae Hwang
Mathematics 2023, 11(18), 3812; https://doi.org/10.3390/math11183812 - 5 Sep 2023
Viewed by 908
Abstract
This paper presents a novel formulation for detecting the secondary synchronization signal in a narrowband Internet of Things communication system. The proposed approach is supported by a noncoherent algorithm that eliminates the need for channel information. A robust joint synchronization scheme is developed [...] Read more.
This paper presents a novel formulation for detecting the secondary synchronization signal in a narrowband Internet of Things communication system. The proposed approach is supported by a noncoherent algorithm that eliminates the need for channel information. A robust joint synchronization scheme is developed by decoupling the estimations of the physical cell identity and the carrier frequency offset. We derive the detection probability of the proposed physical cell identity detector and the mean squared error of the carrier frequency offset estimator, demonstrating their accuracy through simulation results. The performance of the proposed detection scheme is compared with that of existing detection schemes in terms of both estimation accuracy and computational complexity. Experimental results confirm that the proposed synchronization method exhibits superior performance while maintaining relatively lower complexity compared with benchmark methods. Full article
(This article belongs to the Special Issue Mathematical Models for Emerging Internet of Things: Communications, Computing and Security)
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