Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems,...
share announcement

Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications

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

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 7724

Special Issue Editors

Prof. Dr. Andrey Koucheryavy

E-Mail Website
Guest Editor
Telecommunication Networks and Data Transmission Department, St. Petersburg State University of Telecommunications, Saint Petersburg 193232, Russia
Interests: network planning; sensor networks; UAV networks; Internet of Things; tactile internet; 5G and beyond
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed A. Abd El-Latif

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 6131567, Egypt
Interests: post-quantum cryptography; cybersecurity; artificial intelligence of things; AI-based image processing; future networks; information hiding
Special Issues, Collections and Topics in MDPI journals
Dr. Ammar Muthanna

E-Mail Website
Guest Editor
Department of Communication Networks and Data Transmission, Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia
Interests: Internet of Things (IoT); software-defined networking (SDN); 5G; 6G; intelligent edge
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT)-based smart services are becoming increasingly popular and gaining considerable attention due to the ever-increasing presence of wireless networks. Moreover, latency, reliability, and scalability are the main key performance indicators for URLLC applications that can be processed on these devices, such as industrial automation, telemedicine, intelligent transportation, virtual/augmented reality, tactile Internet, holographic-type communications (HTC), telepresence services and Metaverse.

Furthermore, the Edge computing paradigm is introduced as a resourceful architecture for IoT devices. However, to fully utilize the architecture of edge computing for new network planning and IoT applications, many challenges and open issues must be addressed, such as effective optimization architectures for managing computing and storage resources, innovative and efficient schemes for mobility management, intelligent vehicles/unmanned vehicles, privacy and security concerns, and a variety of other AI-led computing environments.

The purpose of this Special Issue is to provide the academic and industrial communities with an excellent venue covering all aspects of current work on emerging trends in future network planning and the Internet of Things.

Potential topics include, but are not limited to, the following:

  • Mathematical modeling of dense IoT networks;
  • Network optimization of dense IoT;
  • Three-dimensional modeling of IoT networks, including 3D multilayer heterogeneous networks;
  • Mathematical modeling of HTC;
  • Novel mathematical methods for inter and intra networking of dense IoT networks;
  • Adaptive routing in high-density and ultra-high-density networks;
  • Mathematical methods for modeling uRLLC;
  • Mathematical modeling of telepresence services;
  • Swarm optimization algorithms for high-density and ultra-high-density networks;
  • Fractal-based high-density and ultra-high-density networks;
  • Chaos theory for IoT network applications;
  • Cybersecurity issues for IoT and UAVs.

Prof. Dr. Andrey Koucheryavy
Dr. Ahmed A. Abd El-Latif
Dr. Ammar Muthanna
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

  • three-dimensional modeling
  • protocols
  • Internet of Things
  • holographic-type communications
  • telepresence services
  • three-dimensional multilayer heterogeneous networks
  • fractals
  • URLLC applications
  • network optimization
  • network planning
  • cybersecurity

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 6850 KiB  
Article
Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment
by Fatmah Y. Assiri and Mahmoud Ragab
Mathematics 2023, 11(19), 4080; https://doi.org/10.3390/math11194080 - 26 Sep 2023
Cited by 1 | Viewed by 1038
Abstract
The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT [...] Read more.
The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

19 pages, 1790 KiB  
Article
An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
by Shangpeng Wang, Liangliang Zhang and Huilong Fan
Mathematics 2023, 11(16), 3456; https://doi.org/10.3390/math11163456 - 09 Aug 2023
Viewed by 767
Abstract
In order to address a problem in the research related to the low stability and communication efficiency issues in the generation of optical communication constellation network topology, there is a critical component for sensing the interaction among satellites. This paper makes a novel [...] Read more.
In order to address a problem in the research related to the low stability and communication efficiency issues in the generation of optical communication constellation network topology, there is a critical component for sensing the interaction among satellites. This paper makes a novel contribution by proposing a multi-constraint optimization algorithm for optical communication constellation network topology generation. The proposed method significantly improves the existing systems by considering multiple attributes that influence the establishment of inter-satellite links and reducing the impact of subjective factors. This unique approach involves calculating the entropy weight of each attribute using the information entropy method based on the degree of change in each indicator. Subsequently, the weights of the indicators are corrected to obtain the objective weight of each attribute. The comprehensive weight of the link, computed based on the initial link attribute values and weights, serves as the decision basis for link selection, thereby forming the satellite network topology. Upon evaluation, the proposed method has shown remarkable superiority over the compared schemes in terms of communication efficiency and stability. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

19 pages, 2813 KiB  
Article
Cluster-Based Vehicle-to-Everything Model with a Shared Cache
by Andrei Vladyko, Gleb Tambovtsev, Elena Podgornaya, Samia Allaoua Chelloug, Reem Alkanhel and Pavel Plotnikov
Mathematics 2023, 11(13), 3017; https://doi.org/10.3390/math11133017 - 07 Jul 2023
Cited by 1 | Viewed by 976
Abstract
This paper presents an analysis of the effectiveness of the element interaction model in a vehicular ad hoc network (VANET). An analysis of the mathematical model and its numerical solution for the system of boundary device interactions in the traditional configuration of roadside [...] Read more.
This paper presents an analysis of the effectiveness of the element interaction model in a vehicular ad hoc network (VANET). An analysis of the mathematical model and its numerical solution for the system of boundary device interactions in the traditional configuration of roadside unit (RSU) placement using single- and dual-channel connection between on-board units (OBU) and RSU is given. In addition, the model efficiency is improved using a clustering approach. The efficiency evaluation is based on calculating the percentage of unprocessed requests generated by OBUs during their mobility, the average power consumption and the magnitude of the delay in transmitting and processing the generated requests in the OBU–RSU system. The traditional and cluster models are compared. The results obtained in this paper show that each of the proposed models can be effectively implemented in mobile nodes and will significantly reduce the overall expected query processing time to improve the organization and algorithmic support of VANET. Along with this, it is shown that the developed approach allows for efficient power consumption when combining RSUs into clusters with a shared cache. The novelty of solving the problems is due to the lack of a comprehensive model that allows the distribution and prediction of the parameters and resources of the system for different computational tasks, which is essential when implementing and using V2X technology to solve the problems of complex management of VANET elements. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

23 pages, 7601 KiB  
Article
Research on Signal Detection of OFDM Systems Based on the LSTM Network Optimized by the Improved Chameleon Swarm Algorithm
by Yunshan Sun, Yuetong Cheng, Ting Liu, Qian Huang, Jianing Guo and Weiling Jin
Mathematics 2023, 11(9), 1989; https://doi.org/10.3390/math11091989 - 23 Apr 2023
Cited by 3 | Viewed by 1177
Abstract
In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance [...] Read more.
In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

24 pages, 4240 KiB  
Article
Adaptive Path Selection Algorithm with Flow Classification for Software-Defined Networks
by Muhammed Nura Yusuf, Kamalrulnizam bin Abu Bakar, Babangida Isyaku, Ahmed Hamza Osman, Maged Nasser and Fatin A. Elhaj
Mathematics 2023, 11(6), 1404; https://doi.org/10.3390/math11061404 - 14 Mar 2023
Cited by 2 | Viewed by 1666
Abstract
Software-Defined Networking (SDN) is a trending architecture that separates controller and forwarding planes. This improves network agility and efficiency. The proliferation of the Internet of Things devices has increased traffic flow volume and its heterogeneity in contemporary networks. Since SDN is a flow-driven [...] Read more.
Software-Defined Networking (SDN) is a trending architecture that separates controller and forwarding planes. This improves network agility and efficiency. The proliferation of the Internet of Things devices has increased traffic flow volume and its heterogeneity in contemporary networks. Since SDN is a flow-driven network, it requires the corresponding rule for each flow in the flowtable. However, the traffic heterogeneity complicates the rules update operation due to varied quality of service requirements and en-route behavior. Some flows are delay-sensitive while others are long-lived with a propensity to consume network buffers, thereby inflicting congestion and delays on the network. The delay-sensitive flows must be routed through a path with minimal delay, while congestion-susceptible flows are guided along a route with adequate capacity. Although several efforts were introduced over the years to efficiently route flows based on different QoS parameters, the current path selection techniques consider either link or switch operation during decisions. Incorporating composite path metrics with flow classification during path selection decisions has not been adequately considered. This paper proposes a technique based on composite metrics with flow classification to differentiate congestion-prone flows and reroute them along appropriate paths to avoid congestion and loss. The technique is integrated into the SDN controller to guide the selection of paths suitable to each traffic class. Compared to other works, the proposed approach improved the path load ratio by 25%, throughput by 35.6%, and packet delivery ratio by 31.7%. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

12 pages, 2987 KiB  
Article
Multi-Story Building Model for Efficient IoT Network Design
by Sergey Bushelenkov, Alexander Paramonov, Ammar Muthanna, Ahmed A. Abd El-Latif, Andrey Koucheryavy, Osama Alfarraj, Paweł Pławiak and Abdelhamied A. Ateya
Mathematics 2023, 11(6), 1403; https://doi.org/10.3390/math11061403 - 14 Mar 2023
Cited by 2 | Viewed by 1266
Abstract
This article presents a new network model for IoT that is based on a multi-story building structure. The model locates network nodes in a regular, cubic lattice-like structure, resulting in an equation for the signal-to-noise ratio (SNR). The study also determines the relationship [...] Read more.
This article presents a new network model for IoT that is based on a multi-story building structure. The model locates network nodes in a regular, cubic lattice-like structure, resulting in an equation for the signal-to-noise ratio (SNR). The study also determines the relationship between traffic density, network density, and SNR. In addition, the article explores the potential of percolation theory in characterizing network functionality. The findings offer a new approach to network design and planning, allowing for selecting a network topology that meets criteria and requirements while ensuring connectivity and improving efficiency. The developed analytical apparatus provides valuable insights into the properties of the network and its applicability to specific conditions. Full article
(This article belongs to the Special Issue Network Planning and Internet of Things: Mathematical Modeling, Connectivity Problems, and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop