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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: 20 July 2024 | Viewed by 1796

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

Published Papers (1 paper)

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Review

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
Viewed by 412
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Smart IoT-Enabled Recommender System for Electric Vehicle Charging Optimization

Xavier Fernando et.al.

Abstract: Electric Vehicles (EVs) present a promising solution to environmental concerns, yet the challenge of energy efficiency persists, necessitating further research. A major obstacle is the insufficient EV charging infrastructure, hindering EV adoption. To address this, deploying adequate infrastructure with suitable charging stations (CS) is crucial for improved connectivity. Real-time access to information about charging station availability, proximity, and wait times is essential. This study explores leveraging the Internet of Things (IoT) for sensor deployment and data transmission to provide timely insights into charging station status. Strategic sensor placement enables predictive analytics for optimizing server access, vehicle scheduling, and reducing wait times. Additionally, a recommender system aids users in locating charging stations with shorter wait times and lower costs. Privacy concerns are addressed through robust privacy protection measures, ensuring secure EV information exchange between charging stations and user locations via routing protocols. Simulation using MATLAB 2020a and cloud-based data storage facilitate validation of the proposed model's efficacy. Metrics such as electricity cost, charging infrastructure utilization, energy optimization, and vehicle charging costs are assessed to validate the model's performance. In summary, this research aims to overcome barriers to EV adoption by integrating IoT technologies, data analytics, and privacy measures to optimize charging infrastructure and enhance the user experience.

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