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Electronics
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Artificial Intelligence and Machine Learning with RFID Technology for IoT
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Artificial Intelligence and Machine Learning with RFID Technology for IoT

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 15015

Special Issue Editors

Dr. Mohammed Abdulhakim Al-Absi

E-Mail Website
Guest Editor
Department of Computer Engineering, Graduate School, Dongseo University, Sasanggu 47011, Korea
Interests: IoT; VANETs; UAVs; AI; cryptology; network security; side-channel attack; deep learning; cloud computing; computer networks and digital communications
Dr. Rui Fu

E-Mail Website
Co-Guest Editor
Blockchain Laboratory of Agriculture and Vegetables, Weifang University of Science and Technology, Weifang 262700, China
Interests: artificial intelligence; VANETs; UAVs/drones; deep learning; logistics transportation; mathematics

Special Issue Information

Dear Colleagues,

With the development of science and technology, information technology has also achieved higher development, of which the internet of things occupies an impotant position. The internet of things is a relatively large scale self organizing network, and RFID technology and sensing equipment are the technical foundation of the internet of things. RFID technology is a new type of non contact automatic identification technology, which achieves the purpose of autonatucally identifying target objects by using radio frequency signals and coupling transmation. Users can modify the information acquired from the data of all the RFID tag readings to analyze the best solution for the practical challenges by integrating Machine Learning algorithms with RFID technology.

The aim of this Special Issue is to bring together researchers to disseminate their recent advances related to the challenges and solutions in building Artificial Intelligence Machine Learning with RFID technology for IoT.

The particular topics of interest include, but are not limited to:

  • Antenna design
  • Security issues and protocols
  • RFID Applications
  • Industrial Internet
  • Green RFID Applications
  • Artificial Intelligence Machine Learning with RFID Devices
  • RFID Data Management
  • Middleware for Internet of Things
  • Reader antennas/systems
  • Cryptography, Cybersecurity, and privacy-enhancing techniques
  • RFID-based infrastructures for Internet of Things
  • RFID for Industry 4.0
  • RFID for Smart Cities
  • RFID manufacturing processes, 3D and inkjet printing
  • RFID sensors
  • Modelling, simulation and implementation of RFID-based systems
  • Near Field Communications
  • IoT and 5G wireless sensing based on RFID concepts

Dr. Mohammed Abdulhakim Al-Absi
Dr. Rui Fu
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 
  • AL and ML 
  • RFID 
  • wierless sensors

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Published Papers (6 papers)

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Research

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19 pages, 5647 KiB  
Article
Using DL Models in the Service Layer to Enhance the Fault Tolerance of IoT Networks
by Sastry Kodanda Rama Jammalamadaka, Bhupati Chokara, Sasi Bhanu Jammalamadaka and Balakrishna Kamesh Duvvuri
Electronics 2024, 13(22), 4334; https://doi.org/10.3390/electronics13224334 - 5 Nov 2024
Cited by 1 | Viewed by 839
Abstract
In an IoT network, the networked servers form a service layer, providing services to the users and the devices. The request to the service servers is routed through the gateway on one side of the services layer and the networked controllers on the [...] Read more.
In an IoT network, the networked servers form a service layer, providing services to the users and the devices. The request to the service servers is routed through the gateway on one side of the services layer and the networked controllers on the other side. Data are transported from the sensors/devices through cluster heads en route to base stations and the controllers to the service servers, where the data are processed and sent for storage in the cloud through gateways. When any device is broken down or becomes non-operational, the inputs are not sensed, creating a gap in the data. The data transmitted from the devices would then become an incomplete flow; such data are not suitable for undertaking data analytics or predictions. The missing data must be first identified as the data flow and estimated or predicated to complete the data before they are transmitted through the cloud for storage and subsequent retrievals. This paper proposes a recurrent (RNN) neural network to predict the missing data. Two models are tested to predict the missing data: the multi-layer perceptron (MLP) model and a long short-term memory (LSTM)-based RNN model. The RNN-based model provides 99.66% accurate data prediction compared to other models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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27 pages, 33375 KiB  
Article
Worker Presence Monitoring in Complex Workplaces Using BLE Beacon-Assisted Multi-Hop IoT Networks Powered by ESP-NOW
by Raihan Uddin, Taewoong Hwang and Insoo Koo
Electronics 2024, 13(21), 4201; https://doi.org/10.3390/electronics13214201 - 26 Oct 2024
Cited by 1 | Viewed by 1267
Abstract
The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as [...] Read more.
The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system’s ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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25 pages, 4614 KiB  
Article
Process Discovery Techniques Recommendation Framework
by Mohammed Abdulhakim Al-Absi and Hind R’bigui
Electronics 2023, 12(14), 3108; https://doi.org/10.3390/electronics12143108 - 17 Jul 2023
Cited by 4 | Viewed by 1762
Abstract
In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting [...] Read more.
In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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44 pages, 6909 KiB  
Article
Inaudible Attack on AI Speakers
by Seyitmammet Saparmammedovich Alchekov, Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi and Hoon Jae Lee
Electronics 2023, 12(8), 1928; https://doi.org/10.3390/electronics12081928 - 19 Apr 2023
Cited by 2 | Viewed by 4480
Abstract
The modern world does not stand still. We used to be surprised that technology could speak, but now voice assistants have become real family members. They do not simply turn on the alarm clock or play music. They communicate with children, help solving [...] Read more.
The modern world does not stand still. We used to be surprised that technology could speak, but now voice assistants have become real family members. They do not simply turn on the alarm clock or play music. They communicate with children, help solving problems, and sometimes even take offense. Since all voice assistants have artificial intelligence, when communicating with the user, they take into account the change in their location, time of day and days of the week, search query history, previous orders in the online store, etc. However, voice assistants, which are part of modern smartphones or smart speakers, pose a threat to their owner’s personal data since their main function is to capture audio commands from the user. Generally, AI smart speakers such as Siri, Google Assistance, Google Home, and so on are moderately harmless. As voice assistants become versatile, like any other product, they can be used for the most nefarious purposes. There are many common attacks that people with bad intentions can use to hack our voice assistant. We show in our experience that a laser beam can control Google Assistance, smart speakers, and Siri. The attacker does not need to make physical contact with the victim’s equipment or interact with the victim; since the attacker’s laser can hit the smart speaker, it can send commands. In our experiments, we achieve a successful attack that allows us to transmit invisible commands by aiming lasers up to 87 m into the microphone. We have discovered the possibility of attacking Android and Siri devices using the built-in voice assistant module through the charging port. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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13 pages, 4095 KiB  
Article
Test Platform for Developing Processes of Autonomous Identification in RFID Systems with Proximity-Range Read/Write Devices
by Bartłomiej Wilczkiewicz, Piotr Jankowski-Mihułowicz and Mariusz Węglarski
Electronics 2023, 12(3), 617; https://doi.org/10.3390/electronics12030617 - 26 Jan 2023
Cited by 5 | Viewed by 1947
Abstract
The subject of a distributed RFID system with proximity-range read/write devices (RWD) is considered in this paper. Possible work scenarios were presented in the scope of industrial implementations and were then tested in a dedicated laboratory set. The development system is based on [...] Read more.
The subject of a distributed RFID system with proximity-range read/write devices (RWD) is considered in this paper. Possible work scenarios were presented in the scope of industrial implementations and were then tested in a dedicated laboratory set. The development system is based on a high-frequency RWD integrated with a Wi-Fi microcontroller unit to create an Internet of things connected with a server (for data exchanging, user interface, etc.) via a wireless local area network. In practical applications, in order to increase the interrogation zone (IZ), there is a tendency to use one RWD with significant output power equipped with a multiplexer for managing several antennas located in the operational space. Such a solution is often economically unprofitable and even impossible to implement, especially in the case of the need to create the large IZ. Responding to market demand, the authors propose a distributed system developed on the basis of several cheap RFID reader modules and a few freely available hardware/software tools. They created the fully functional RFID platform and confirmed its usefulness in static and dynamic systems of object identification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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Review

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21 pages, 849 KiB  
Review
Review of Time Domain Electronic Medical Record Taxonomies in the Application of Machine Learning
by Haider Ali, Imran Khan Niazi, Brian K. Russell, Catherine Crofts, Samaneh Madanian and David White
Electronics 2023, 12(3), 554; https://doi.org/10.3390/electronics12030554 - 21 Jan 2023
Cited by 4 | Viewed by 2927
Abstract
Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring [...] Read more.
Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring real-time health trends. This review focuses on the time-series data needed to construct complete EMRs by identifying paradigms that fall within the scope of the application of artificial intelligence (AI) based on the principles of translational medicine. (1) Background: The question addressed in this study is: What are the taxonomies present in the field of the application of machine learning on EMRs? (2) Methods: Scopus, Web of Science, and PubMed were searched for relevant records. The records were then filtered based on a PRISMA review process. The taxonomies were then identified after reviewing the selected documents; (3) Results: A total of five main topics were identified, and the subheadings are discussed in this review; (4) Conclusions: Each aspect of the medical data pipeline needs constant collaboration and update for the proposed solutions to be useful and adaptable in real-world scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with RFID Technology for IoT)
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