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

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 16631

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Special Issue Editors

Dr. Mamun Abu-Tair

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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

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Guest Editor

Department of Computer Science, University of Huddersfield, Huddersfield, UK
Interests: wireless networks; cyber security; smart cities and intelligent transportation systems (ITS)
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Dr. Dhiah el Diehn I. Abou-Tair

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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

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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

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Published Papers (7 papers)

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Research

16 pages, 3776 KiB

Open AccessArticle

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

by Ray-I Chang, Tzu-Chieh Lin and Jeng-Wei Lin

Electronics 2024, 13(13), 2506; https://doi.org/10.3390/electronics13132506 - 26 Jun 2024

Viewed by 1245

Abstract

With the development of sensor and communication technologies, the Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerging. For example, a driver with the smart key in their pocket can push the start button to start a car. At the same time, security issues in the push-to-start scenario are pervasive, such as smart key forgery. In this study, we propose a vehicle Passive Entry Passive Start (PEPS) system that adopts deep learning algorithms to recognize the driver using the electrocardiogram (ECG) signals measured on the driver’s smart watch. ECG signals are used for personal identification. Smart watches, serving as new smart keys of the PEPS system, can improve convenience and security. In the experiment, we consider commercial smart watches capable of sensing ECG signals. The sample rate and precision are typically lower than those of a 12-lead ECG used in hospitals. The experimental results show that Long Short-Term Memory (LSTM) models achieve the best accuracy score for identity recognition (91%) when a single ECG cycle is used. However, it takes at least 30 min for training. The training of a personalized Auto Encoder model takes only 5 min for each subject. When 15 continuous ECG cycles are sensed and used, this can achieve 100% identity accuracy. As the personalized Auto Encoder model is an unsupervised learning one-class recognizer, it can be trained using only the driver’s ECG signal. This will simplify the management of ECG recordings extremely, as well as the integration of the proposed technology into PEPS vehicles. A FIDO (Fast Identify Online)-like environment for the proposed PEPS system is discussed. Public key cryptography is adopted for communication between the smart watch and the PEPS car. The driver is first verified on the smart watch via local ECG biometric authentication, and then identified by the PEPS car. Phishing attacks, MITM (man in the middle) attacks, and replay attacks can be effectively prevented. Full article

(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)

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21 pages, 7045 KiB

Open AccessArticle

Development of an On-Shaft Vibration Sensing Module for Machine Wearable Rotor Imbalance Monitoring

by Zhaozong Meng, Yirou Liu, Yang Chen, Zhen Cheng, Guojin Feng, Hao Zhang, Nan Gao and Zonghua Zhang

Electronics 2024, 13(13), 2466; https://doi.org/10.3390/electronics13132466 - 24 Jun 2024

Viewed by 3627

Abstract

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; which may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices. Full article

(This article belongs to the Special Issue Communication, Sensing and Computing for Intelligent Internet of Things Enabled Applications)

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23 pages, 4937 KiB

Open AccessArticle

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 - 9 Jan 2024

Cited by 5 | Viewed by 1892

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 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/cm

^{2}

). 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

Open AccessArticle

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 - 7 Dec 2023

Cited by 2 | Viewed by 4024

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, 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

Open AccessArticle

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 1175

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 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

Open AccessArticle

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 - 9 Sep 2023

Cited by 1 | Viewed by 1425

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 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

Open AccessArticle

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 - 4 Sep 2023

Cited by 1 | Viewed by 2091

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 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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