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Advancements and Challenges in IoT Communication Technologies for a Connected World

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 10 June 2025 | Viewed by 7736

Special Issue Editors

Dr. Francesco Paolo Chietera

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy
Interests: RFID technologies; antennas; 3D-printing; additive manufacturing in electromagnetics; IoT enabling technologies; smart electromagnetic devices
Dr. Xiao Tang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: 6G networks; information security; UAV communications; Internet of Things; machine learning; game theory
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Ria

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Pisa, Italy
Interests: low-voltage; sensor interfaces; IoT system; integrated circuits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks contributions at the intersection of IoT communication technologies and their applications in creating a more interconnected society. It will focus on critical areas such as the integration of sensors into next-generation communication systems, new communication paradigms, and technologies such as Internet of Drones (IoD), Industrial Internet of Things (IIoT), machine-to-machine (M2M) communications, RFID, and zero-power communications, and their applications in smart agriculture and smart cities. The aim is to address the scientific and technological challenges in enhancing data transmission, processing capabilities, and energy efficiency across diverse IoT systems. Contributions should explore, among other topics, progress in RFID for efficient data gathering, the adoption of zero-power technologies to prolong the lifespan of IoT components, and the exploitation of IoD and IIoT to enhance the operational efficiency and communication system performance. For this Special Issue, we encourage submissions of original research articles, comprehensive reviews, and case studies that showcase novel approaches to overcoming the limitations of current IoT communication technologies, with a particular emphasis on their practical implications in smart agriculture and smart city development.

Dr. Francesco Paolo Chietera
Dr. Xiao Tang
Dr. Andrea Ria
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. Sensors 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 2600 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
  • communication
  • sensors

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Published Papers (7 papers)

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Research

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50 pages, 7835 KiB  
Article
Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System
by Marilena Ianculescu, Victor-Ștefan Constantin, Andreea-Maria Gușatu, Mihail-Cristian Petrache, Alina-Georgiana Mihăescu, Ovidiu Bica and Adriana Alexandru
Sensors 2025, 25(7), 2292; https://doi.org/10.3390/s25072292 - 4 Apr 2025
Viewed by 391
Abstract
The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, [...] Read more.
The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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23 pages, 8545 KiB  
Article
A Mold Damage Monitoring Algorithm for Power Metallurgy Molding Machines Using Bidirectional Long Short-Term Memory on an Internet of Things Platform
by Hao-Pu Lin, Yuan-Chieh Chen, Chin-Chuan Han, Yu-Chi Wu and Jin-Yuan Lin
Sensors 2025, 25(7), 2143; https://doi.org/10.3390/s25072143 - 28 Mar 2025
Viewed by 230
Abstract
In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) and an embedded system are first used to acquire vibration data from a powder metallurgy molding machine. These data are collected [...] Read more.
In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) and an embedded system are first used to acquire vibration data from a powder metallurgy molding machine. These data are collected on an Internet of Things (IoT) platform using the Message Queueing Telemetry Transport (MQTT) protocol. For data analysis, the vibration signal on the Z axis is segmented to label the contact section of the upper and middle molds, and the corresponding vibration data of the stamping friction on the X, Y, and Z axes are extracted. Using only historical vibration data from normal stamping, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with an attention mechanism is trained to predict normal stamping vibrations several minutes in advance. By comparing the predicted stamping vibrations with the observed data at the current time, the mean square errors (MSEs) are calculated to evaluate the health status of the mold. Several ablation experiments were conducted to assess the performance of the trained model. The average MSE values for normal samples and abnormal samples were smaller than 0.5 and larger than 1.0, respectively. The experimental results confirm that the trained prediction model and evaluation indicators can effectively notify operators in advance. An early warning system using vibration data for mold damage was successfully implemented, enhancing predictive maintenance. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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21 pages, 7806 KiB  
Article
Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool
by Yu-Chi Wu, Yu-Jung Huang, Chin-Chuan Han, Yuan-Yang Cheng and Chao-Shu Chang
Sensors 2025, 25(7), 1994; https://doi.org/10.3390/s25071994 - 22 Mar 2025
Viewed by 375
Abstract
Stroke is the fifth leading cause of death in Taiwan. In the process of stroke treatment, rehabilitation for gait recovery is one of the most critical aspects of treatment. The Gait Assessment and Intervention Tool (G.A.I.T.) is currently used in clinical practice to [...] Read more.
Stroke is the fifth leading cause of death in Taiwan. In the process of stroke treatment, rehabilitation for gait recovery is one of the most critical aspects of treatment. The Gait Assessment and Intervention Tool (G.A.I.T.) is currently used in clinical practice to assess the gait recovery level; however, G.A.I.T. heavily depends on physician training and clinical judgment. With the advancement of technology, today’s small, lightweight inertial measurement unit (IMU) wearable sensors are rapidly revolutionizing gait assessment and may be incorporated into routine clinical practice. In this paper, we developed a gait data acquisition and analysis system based on IMU wearable devices, proposed a simple yet accurate calibration process to reduce the IMU drifting errors, designed a machine learning algorithm to obtain real-time coordinates from IMU data, computed gait parameters, and derived a formula for G.A.I.T. scores with significant correlation with the physician’s observational scores. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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32 pages, 1916 KiB  
Article
An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors
by Amir Ali, Teodoro Montanaro, Ilaria Sergi, Simone Carrisi, Daniele Galli, Cosimo Distante and Luigi Patrono
Sensors 2025, 25(6), 1735; https://doi.org/10.3390/s25061735 - 11 Mar 2025
Viewed by 782
Abstract
The aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for [...] Read more.
The aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for a consultation. Without historical insights into common behaviors and potential anomalies detected with unobtrusive techniques (e.g., non-wearable devices), timely and informed medical interventions become challenging. To address this, we propose an edge-based Internet of Things (IoT) framework that enables real-time monitoring and anomaly detection using non-wearable sensors to assist doctors and caregivers in assessing the health of elderly patients. By processing data locally, the system minimizes privacy concerns and ensures immediate data availability, allowing healthcare professionals to detect unusual behavioral patterns early. The system employs advanced machine learning (ML) models to identify deviations that may indicate potential health risks. A prototype of our system has been developed to test its feasibility and demonstrate, through the application of two of the most frequently used ML models, i.e., isolation forest and Long Short-Term Memory (LSTM) networks, that it can provide scalability, efficiency, and reliability in the context of elderly care. Further, the provided dashboard enables caregivers and healthcare professionals to access real-time alerts and longitudinal trends, facilitating proactive interventions. The proposed approach improves healthcare responsiveness by providing instant insights into patient behavior, facilitating more accurate diagnoses and interventions. This study lays the groundwork for future advancements in the field and offers valuable insights for the research community to harness the full potential of combining edge computing, artificial intelligence (AI), and the IoT in elderly care. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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19 pages, 421 KiB  
Article
Robust Access Control for Secure IoT Outsourcing with Leakage Resilience
by Khaled Riad
Sensors 2025, 25(3), 625; https://doi.org/10.3390/s25030625 - 22 Jan 2025
Viewed by 609
Abstract
The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust [...] Read more.
The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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Review

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57 pages, 21747 KiB  
Review
Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview
by Paolo Visconti, Giuseppe Rausa, Carolina Del-Valle-Soto, Ramiro Velázquez, Donato Cafagna and Roberto De Fazio
Sensors 2025, 25(2), 562; https://doi.org/10.3390/s25020562 - 19 Jan 2025
Cited by 1 | Viewed by 3987
Abstract
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, [...] Read more.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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Other

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25 pages, 3541 KiB  
Systematic Review
IoT Sensing for Advanced Irrigation Management: A Systematic Review of Trends, Challenges, and Future Prospects
by Ahmed A. Abdelmoneim, Hilda N. Kimaita, Christa M. Al Kalaany, Bilal Derardja, Giovanna Dragonetti and Roula Khadra
Sensors 2025, 25(7), 2291; https://doi.org/10.3390/s25072291 - 4 Apr 2025
Viewed by 555
Abstract
Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management [...] Read more.
Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management was investigated. The study aimed to identify key research trends and technological developments in the field. Using VOSviewer (CWTS, Leiden, The Netherlands) for bibliometric mapping, the influential research clusters were identified. The analysis revealed a significant rise in scholarly interest, with peak activity between 2020 and 2022, and a shift towards interdisciplinary and applied research. Additionally, the content analysis revealed prevalent agricultural applications, frequently employed microcontroller units (MCUs), widely used sensors, and trends in communication technologies such as the increasing adoption of low-power, scalable communication protocols for real-time data acquisition. This study not only offers a comprehensive overview of the current status of IoT integration in smart irrigation but also highlights the technological advancements. Future research directions include integrating IoT with emerging technologies such as artificial intelligence, edge computing, and blockchain to enhance decision-support systems and predictive irrigation strategies. By examining the transformative potential of IoT, this study provides valuable insights for researchers and practitioners seeking to enhance agricultural productivity, optimize resource use, and improve sustainability. Full article
(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)
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