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Technologies
Special Issues
IoT-Enabling Technologies and Applications
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IoT-Enabling Technologies and Applications

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 10758

Special Issue Editor

Prof. Dr. Xavier Fernando

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada
Interests: wireless communications; 6G+ wireless networks, signal processing; optical communications; optical–wireless communications; machine learning; IoT; tracking and localization; underground communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will cover original research and extensive review articles on IoT-enabling technologies and applications, including, but not limited to, the following topics:

  • IoT architectures and their applications;
  • Challenges and issues in IoT such as security, privacy, and environmental impacts;
  • Wireless sensor networks and their applications in IoT systems;
  • Integrated Sensing and Communications (ISAC) in IoT systems;
  • Challenges in aerial, terrestrial and below earth IoT networks;
  • Intelligent Reflecting Surfaces in IoT networks;
  • Cloud, Fog and Edge computing in IoT systems;
  • Big data analytics and its use in IoT systems;
  • Embedded systems and their role in IoT systems;
  • Semantic search engines and their use in IoT systems;
  • Machine learning and artificial intelligence for IoT applications;
  • Smart cities, autonomous vehicles and other user cases of IoT technologies;
  • Digital twins and their applications with IoT.

Prof. Dr. Xavier Fernando
Guest Editor

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. Technologies is an international peer-reviewed open access monthly 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 1600 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
  • machine learning
  • artificial intelligence
  • wireless communications
  • massive connectivity
  • intelligent reflecting surfaces
  • digital twins
  • blockchain
  • large-scale modeling
  • cloud computing

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Published Papers (5 papers)

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Research

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18 pages, 12726 KiB  
Article
Quad-Band Rectifier Circuit Design for IoT Applications
by Ioannis D. Bougas, Maria S. Papadopoulou, Achilles D. Boursianis, Sotirios Sotiroudis, Zaharias D. Zaharis and Sotirios K. Goudos
Technologies 2024, 12(10), 188; https://doi.org/10.3390/technologies12100188 - 2 Oct 2024
Viewed by 660
Abstract
In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates [...] Read more.
In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4, 3.5, 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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23 pages, 6929 KiB  
Article
IoT Energy Management System Based on a Wireless Sensor/Actuator Network
by Omar Arzate-Rivas, Víctor Sámano-Ortega, Juan Martínez-Nolasco, Mauro Santoyo-Mora, Coral Martínez-Nolasco and Roxana De León-Lomelí
Technologies 2024, 12(9), 140; https://doi.org/10.3390/technologies12090140 - 24 Aug 2024
Viewed by 1809
Abstract
The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as [...] Read more.
The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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21 pages, 2915 KiB  
Article
A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
by Padmanabhan Amudhavalli, Rahiman Zahira, Subramaniam Umashankar and Xavier N. Fernando
Technologies 2024, 12(8), 137; https://doi.org/10.3390/technologies12080137 - 20 Aug 2024
Viewed by 1680
Abstract
Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including [...] Read more.
Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm’s performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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21 pages, 99916 KiB  
Article
Analysis and Development of an IoT System for an Agrivoltaics Plant
by Francesco Zito, Nicola Ivan Giannoccaro, Roberto Serio and Sergio Strazzella
Technologies 2024, 12(7), 106; https://doi.org/10.3390/technologies12070106 - 7 Jul 2024
Viewed by 1778
Abstract
This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system [...] Read more.
This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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Review

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34 pages, 1315 KiB  
Review
A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions
by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi and Mohammad N. Alanazi
Technologies 2024, 12(6), 81; https://doi.org/10.3390/technologies12060081 - 3 Jun 2024
Cited by 4 | Viewed by 2909
Abstract
Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions [...] Read more.
Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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