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Emerging IoT Sensor Network Technologies and Applications
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Emerging IoT Sensor Network Technologies and Applications

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Networks".

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 9844

Editors

Dr. Alexandru Iovanovici

E-Mail Website
Guest Editor
Department of Computers and Information Technology, Politehnica University Timisoara, 300086 Timisoara, Romania
Interests: IoT devices; wireless sensor networks; smart medical devices
Dr. Lucian Prodan

E-Mail Website
Guest Editor
Department of Computers and Information Technology, Politehnica University Timisoara, 300086 Timisoara, Romania
Interests: computing architectures; reconfigurable systems; system design and co-design; system reliability; system testing

Special Issue Information

Dear Colleagues,

The rapid evolution of Internet of Things (IoT) technologies has transformed the way we interact with the physical world, enabling seamless integration of sensor networks into diverse applications. IoT sensor networks, which consist of interconnected devices capable of collecting, processing, and transmitting data, serve as the backbone of this transformation. From environmental monitoring and healthcare to industrial automation and smart cities, these networks are driving innovation across domains.

At the heart of this field lies the convergence of advanced sensing technologies, low-power wireless communication protocols, and data-driven analytics, which together enable unprecedented levels of automation and insight. The emergence of 5G, edge computing, and AI-powered analytics further amplifies the potential of IoT sensor networks, allowing for faster, more efficient, and context-aware systems.

The importance of research in IoT sensor networks cannot be overstated, as these technologies address critical challenges, including resource efficiency, real-time decision-making, and scalable deployments. By pushing the boundaries of sensing, connectivity, and data processing, this field continues to pave the way for groundbreaking applications that improve quality of life, enhance productivity, and contribute to sustainable development.

This Special Issue aims to bring together cutting-edge research and innovative applications, showcasing how emerging IoT sensor network technologies are shaping the future and addressing the complexities of a connected world.

This Special Issue aims to explore advancements in IoT sensor network technologies and their applications, highlighting innovative solutions that address emerging challenges in connectivity, sensing, and data processing. These topics align closely with the scope of Electronics, which focuses on cutting-edge research in electronic systems, devices, and applications. By emphasizing the intersection of electronics and IoT, this issue showcases the pivotal role of electronic innovations in shaping the future of connected systems and intelligent applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Next-Generation IoT Sensors: Development of novel sensors with enhanced sensitivity, energy efficiency, and miniaturization for IoT applications.
  2. Low-Power Communication Protocols: Advances in wireless communication technologies for resource-constrained IoT sensor networks.
  3. Edge Computing in IoT Sensor Networks: Integration of edge computing to enable real-time data processing and decision-making.
  4. AI and Machine Learning for IoT: Application of AI and ML techniques for efficient data analysis and predictive modeling in IoT networks.
  5. 5G and Beyond for IoT Connectivity: Role of advanced networks in supporting scalable and high-performance IoT sensor deployments.
  6. Security and Privacy in IoT Sensor Networks: Solutions for safeguarding data integrity, confidentiality, and resilience against cyber threats.
  7. IoT for Smart Cities: Using sensor networks to optimize urban infrastructure, traffic management, and environmental monitoring.
  8. Industrial IoT (IIoT) Applications: Use of IoT sensor networks in manufacturing, supply chain optimization, and predictive maintenance.
  9. Energy Harvesting for IoT Sensors: Innovations in self-powered IoT sensors using renewable energy sources.
  10. Environmental and Healthcare Applications: Deployment of IoT sensor networks for environmental protection and advanced healthcare monitoring.

Dr. Alexandru Iovanovici
Dr. Lucian Prodan
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 sensor networks
  • edge computing
  • wireless communication protocols
  • AI and machine learning in IoT
  • 5G connectivity
  • low-power electronics
  • IoT security and privacy
  • smart cities
  • Industrial IoT (IIoT)
  • energy-efficient sensors

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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22 pages, 1081 KB  
Article
Spatio-Temporal Trajectory-Driven Dynamic TDMA Scheduling for UAV-Assisted Wireless-Powered Communication Networks
by Siliang Gong, Kaiyang Qu, Hongfei Wang, Yaopei Wang, Hanyao Huang, Peixin Qu and Qinghua Chen
Electronics 2026, 15(9), 1861; https://doi.org/10.3390/electronics15091861 - 28 Apr 2026
Viewed by 555
Abstract
UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) [...] Read more.
UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV’s mobility to provide seamless spatial coverage. By jointly optimizing the UAV’s flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring quality of service-based fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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22 pages, 4742 KB  
Article
A Novel E-Nose Architecture Based on Virtual Sensor-Augmented Embedded Intelligence for a Real-Time In-Vehicle Carbon Monoxide Concentration Estimation System
by Dharmendra Kumar, Anup Kumar Rabha, Ashutosh Mishra, Rakesh Shrestha and Navin Singh Rajput
Electronics 2026, 15(8), 1671; https://doi.org/10.3390/electronics15081671 - 16 Apr 2026
Cited by 2 | Viewed by 1352
Abstract
The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous [...] Read more.
The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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22 pages, 2375 KB  
Article
A Comparative Performance Study of Parallel MQTT and RS485 Communication Architectures for High-Frequency IoT Sensing
by Hyun Jun Kim and Meong Hun Lee
Electronics 2026, 15(4), 760; https://doi.org/10.3390/electronics15040760 - 11 Feb 2026
Cited by 4 | Viewed by 1384
Abstract
High-frequency data collection in large-scale IoT sensing systems requires communication architectures that can maintain low latency and stable throughput as sensor density increases. Conventional RS485-based polling structures suffer from rapid performance degradation under multi-node and high-rate conditions due to their sequential communication model. [...] Read more.
High-frequency data collection in large-scale IoT sensing systems requires communication architectures that can maintain low latency and stable throughput as sensor density increases. Conventional RS485-based polling structures suffer from rapid performance degradation under multi-node and high-rate conditions due to their sequential communication model. In this study, we present a comparative performance analysis of parallel MQTT and RS485 communication architectures for high-frequency IoT sensing. The proposed parallel MQTT structure is implemented with topic-level parallelism, QoS-based reliability control, and non-blocking scheduling, and its performance is quantitatively evaluated under multi-sensor experimental conditions. Experimental results show that the parallel MQTT architecture achieves lower average latency (98 ms vs. 178 ms), higher message success rate (99.2% vs. 94.5%), and higher throughput compared to the RS485-based system. In addition, we analyze message loss behavior under high-frequency operation and examine how topic hierarchy and asynchronous processing affect broker-side congestion. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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25 pages, 2011 KB  
Article
Assessing the Adequacy of MQTT and ZeroMQ for 5G-Enabled V2X Networks
by Aditya Timalsina, Naba Raj Khatiwoda, Babu R. Dawadi, Ashutosh Bohara, Shashidhar R. Joshi, Carlos T. Calafate and Pietro Manzoni
Electronics 2025, 14(22), 4509; https://doi.org/10.3390/electronics14224509 - 18 Nov 2025
Cited by 2 | Viewed by 2571
Abstract
The advent of fifth-generation (5G) networks has enabled cellular vehicle-to-everything (C-V2X) communication, requiring the efficient delivery of large volumes of real-time vehicular data under stringent latency and reliability constraints. At the application layer, Message Queuing Telemetry Transport (MQTT) and ZeroMQ have emerged as [...] Read more.
The advent of fifth-generation (5G) networks has enabled cellular vehicle-to-everything (C-V2X) communication, requiring the efficient delivery of large volumes of real-time vehicular data under stringent latency and reliability constraints. At the application layer, Message Queuing Telemetry Transport (MQTT) and ZeroMQ have emerged as candidate protocols; however, their comparative performance in vehicular networking contexts remains insufficiently examined. This work presents a simulation-based evaluation of MQTT and ZeroMQ using OMNeT++, integrating INET for protocol modeling, Veins for vehicular mobility, and Simu5G for cellular network operations. We developed custom protocol modules and assessed them under diverse traffic conditions, analyzing key metrics such as end-to-end latency, message overhead, and scalability. Our results reveal that ZeroMQ achieves lower latency in moderate traffic scenarios, whereas MQTT demonstrates superior reliability and efficiency under high traffic loads, offering valuable insights for selecting application-layer protocols in C-V2X environments. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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20 pages, 2856 KB  
Article
Privacy-Preserving Federated Review Analytics with Data Quality Optimization for Heterogeneous IoT Platforms
by Jiantao Xu, Liu Jin and Chunhua Su
Electronics 2025, 14(19), 3816; https://doi.org/10.3390/electronics14193816 - 26 Sep 2025
Cited by 1 | Viewed by 1586
Abstract
The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake [...] Read more.
The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake reviews, data quality inconsistencies, and significant privacy risks. Traditional centralized analytics fail in this landscape due to data privacy regulations and the sheer scale of distributed data. To address this, we propose FedDQ, a federated learning framework for Privacy-Preserving Federated Review Analytics with Data Quality Optimization. FedDQ introduces a multi-faceted data quality assessment module that operates locally on each IoT device, evaluating review data based on textual coherence, behavioral patterns, and cross-modal consistency without exposing raw data. These quality scores are then used to orchestrate a quality-aware aggregation mechanism at the server, prioritizing contributions from high-quality, reliable clients. Furthermore, our framework incorporates differential privacy and models system heterogeneity to ensure robustness and practical applicability in resource-constrained IoT environments. Extensive experiments on multiple real-world datasets show that FedDQ significantly outperforms baseline federated learning methods in accuracy, convergence speed, and resilience to data poisoning attacks, achieving up to a 13.8% improvement in F1-score under highly heterogeneous and noisy conditions while preserving user privacy. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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Review

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40 pages, 2153 KB  
Review
DeepChainIoT: Exploring the Mutual Enhancement of Blockchain and Deep Neural Networks (DNNs) in the Internet of Things (IoT)
by Sabina Sapkota, Yining Hu, Asif Gill and Farookh Khadeer Hussain
Electronics 2025, 14(17), 3395; https://doi.org/10.3390/electronics14173395 - 26 Aug 2025
Cited by 4 | Viewed by 1548
Abstract
The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited [...] Read more.
The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing. Full article
(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)
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