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Electronics
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Communication, Sensing and Computing for Intelligent Internet of Things Enabled...
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Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 8385

Special Issue Editors

Dr. Mamun Abu-Tair

E-Mail Website
Guest Editor
British Telecom Ireland Innovation Centre (BTIIC), School of Computing, Ulster University, Belfast, UK
Interests: IoT; cybersecurity; computer systems and networking; wireless sensor networks; data-center communication and cloud computing; wireless networks and immersive telecommunications
Dr. Soufiene Djahel

E-Mail Website
Guest Editor
Department of Computer Science, University of Huddersfield, Huddersfield, UK
Interests: wireless networks; cyber security; smart cities and intelligent transportation systems (ITS)
Special Issues, Collections and Topics in MDPI journals
Dr. Dhiah el Diehn I. Abou-Tair

E-Mail Website
Guest Editor
Department of Computer Science, School of Electrical Engineering and Information Technology, German Jordanian University, Amman, Jordan
Interests: privacy-enhancing technologies; security; system of systems; internet of things
Dr. Philip Perry

E-Mail Website
Guest Editor
British Telecom Ireland Innovation Centre (BTIIC), School of Computing, Ulster University, Coleraine BT37 0QB, UK
Interests: computer systems and networking; wireless sensor networks; data-center communication; cloud computing and wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of things (IoT) technology is increasingly pervasive in all aspects of our lives, and its use is anticipated to significantly increase in future smart cities to support their revolutionary applications and advanced concepts such as smart manufacturing, digital twins, smart transportation, and smart healthcare. The network connectivity technology required to support the above applications’ strict operational constraints, such as 5G and B5G, the rise and the ever-expanding spectrum of artificial intelligence techniques usage, and the computational power available at the cloud or edge level, enabled the design and development of a new class of applications in which the IoT acts as a bridge between sensing technologies and cloud/edge computing hosted applications.

It is anticipated that intelligent IoT-based applications will proliferate, contributing to the infrastructure supporting smart cities services. This trend, in turn, is going to impose unprecedented pressure on network connectivity as well as edge and cloud computational power. Therefore, the main aim of this Special Issue is to seek high-quality submissions that highlight emerging intelligent IoT-enabled applications and propose original contributions to solve their associated key challenges. Authors are invited to submit original contributions that address the following topics:

  • IoT applications for smart cities;
  • Edge computing frameworks for intelligent IoT-enabled applications;
  • Security and privacy issues for intelligent IoT-enabled applications;
  • Blockchain technologies for IoT-enabled applications;
  • Cloud computing for intelligent IoT-enabled applications;
  • IoT-enabled digital twins;
  • AI-enabled IoT applications;
  • IoT interoperability and multi-platform integration;
  • Advanced IoT data management, mining, and analytics;
  • IoT big data management and predictive analysis;
  • Resource management techniques for IoT-enabled applications;
  • IoT for smart manufacturing (Industry 5.0) and smart spaces;
  • Mobility, localization, and context-adaptive IoT;
  • Edge computing, fog computing, and IoT;
  • Federated learning for IoT networks;
  • SDN, NFV, and IoT.

Dr. Mamun Abu-Tair
Dr. Soufiene Djahel
Dr. Dhiah el Diehn I. Abou-Tair
Dr. Philip Perry
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

  • IoT
  • cloud computing
  • edge computing 5G
  • B5G
  • digital twin

Published Papers (5 papers)

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Research

23 pages, 4937 KiB  
Article
Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications
by Ana Belén Paredes-Baños, Angel Molina-Garcia, Antonio Mateo-Aroca and José Javier López-Cascales
Electronics 2024, 13(2), 296; https://doi.org/10.3390/electronics13020296 - 09 Jan 2024
Viewed by 982
Abstract
This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and [...] Read more.
This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels. Full article
(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)
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26 pages, 20992 KiB  
Article
Integrating Lorenz Hyperchaotic Encryption with Ring Oscillator Physically Unclonable Functions (RO-PUFs) for High-Throughput Internet of Things (IoT) Applications
by Alexander Magyari and Yuhua Chen
Electronics 2023, 12(24), 4929; https://doi.org/10.3390/electronics12244929 - 07 Dec 2023
Cited by 1 | Viewed by 3678
Abstract
With the combined call for increased network throughput and security comes the need for high-bandwidth, unconditionally secure systems. Through the combination of true random number generators (TRNGs) for unique seed values, and four-dimensional Lorenz hyperchaotic systems implemented on a Stratix 10 Intel FPGA, [...] Read more.
With the combined call for increased network throughput and security comes the need for high-bandwidth, unconditionally secure systems. Through the combination of true random number generators (TRNGs) for unique seed values, and four-dimensional Lorenz hyperchaotic systems implemented on a Stratix 10 Intel FPGA, we are able to implement 60 MB/s encryption/decryption schemes with 0% data loss on an unconditionally secure system with the NIST standard using less than 400 mW. Further, the TRNG implementation allows for unique encryption outputs for similar images while still enabling proper decryption. Histogram and adjacent pixel analysis on sample images demonstrate that without the key, it is not possible to extract the plain text from the encrypted image. This encryption scheme was implemented via PCIe for testing and analysis. Full article
(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)
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16 pages, 4465 KiB  
Article
Bluetooth 5.0 Suitability Assessment for Emergency Response within Fire Environments
by Brendan Black, Joseph Rafferty, Jose Santos, Andrew Ennis, Philip Perry and Maurice McKee
Electronics 2023, 12(22), 4599; https://doi.org/10.3390/electronics12224599 - 10 Nov 2023
Viewed by 738
Abstract
Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire [...] Read more.
Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual’s location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0’s performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters. Full article
(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)
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15 pages, 759 KiB  
Article
Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
by Mnassar Alyami, Abdulmajeed Alghamdi, Mohammed A. Alkhowaiter, Cliff Zou and Yan Solihin
Electronics 2023, 12(18), 3816; https://doi.org/10.3390/electronics12183816 - 09 Sep 2023
Viewed by 796
Abstract
Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the [...] Read more.
Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client–server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98% to 63%, close to random guessing. Meanwhile, the real-world data transmission experiments show that the added latency is reasonable, less than 21%, while the added packet header overhead is only about 5%. Full article
(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)
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32 pages, 9865 KiB  
Article
Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks
by Samson Kahsay Gebresilassie, Joseph Rafferty, Liming Chen, Zhan Cui and Mamun Abu-Tair
Electronics 2023, 12(17), 3731; https://doi.org/10.3390/electronics12173731 - 04 Sep 2023
Viewed by 1304
Abstract
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are [...] Read more.
The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures. Full article
(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)
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