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

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 25165

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Dr. Raffaele Bruno

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Institute for Informatics and Telematics (IIT), National Research Council of Italy (CNR), Via G. Moruzzi, 1, I-56124 Pisa, Italy
Interests: MAC protocols for wireless networks; architectures and protocols for the Internet of Things; vehicular networks; 5G networks; smart transportation; smart grids and smart buildings
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Dr. Petros S. Bithas

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Associate Professor, Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Thesi Skliro, 34400 Evia, Greece
Interests: stochastic modeling of wireless communication channels; design and performance analysis of communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the section ‘Internet of Things’ is now compiling a collection of papers submitted by Editorial Board Members (EBMs) and outstanding scholars in the field.

We welcome the submission of original papers and review articles that present theoretical and applicative advances, new experimental discoveries, and novel technological improvements regarding the Internet of Things. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collated into a printed book after the deadline and be promoted.

We would also like to take this opportunity to invite more excellent scholars to join the section ‘Internet of Things’ so that we can work together to develop this exciting field of research.

Dr. Raffaele Bruno
Dr. Petros S. Bithas
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-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

internet of multimedia things
industrial Internet of Things (IIoT)
underwater IoT communication and networks
machine-type communications
low-power and energy harvesting technologies
real-time systems for the IoT
service middleware and device management for the IoT
privacy, security, and trust in IoT systems
cyber–physical system (CPS) platforms
edge/fog/cloud computing in the IoT
data management and mining platforms for the IoT
IoT architectures and standards
future internet design for the IoT
IoT pilots and testbeds
5G and beyond 5G architectures and protocols for the IoT
AI/ML and distributed intelligence for the IoT
IoT applications and uses (smart factory, smart city, smart health, smart transportation, and smart agriculture)

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Related Special Issue

Feature Papers in the Internet of Things Section 2024 in Sensors (12 articles)

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Research

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22 pages, 1308 KB

Open AccessArticle

From Edge Transformer to IoT Decisions: Offloaded Embeddings for Lightweight Intrusion Detection

by Frédéric Adjewa, Moez Esseghir and Leïla Merghem-Boulahia

Sensors 2026, 26(2), 356; https://doi.org/10.3390/s26020356 - 6 Jan 2026

Cited by 2 | Viewed by 963

Abstract

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is enabling a new class of intelligent applications. Specifically, Large Language Models (LLMs) are emerging as powerful tools not only for natural language understanding but also for enhancing IoT security. However, the integration of these computationally intensive models into resource-constrained IoT environments presents significant challenges. This paper provides an in-depth examination of how LLMs can be adapted to secure IoT ecosystems. We identify key application areas, discuss major challenges, and propose optimization strategies for resource-limited settings. Our primary contribution is a novel collaborative embeddings offloading mechanism for IoT intrusion detection named SEED (Semantic Embeddings for Efficient Detection). This system leverages a lightweight, fine-tuned BERT model, chosen for its proven contextual and semantic understanding of sequences, to generate rich network embeddings at the edge. A compact neural network deployed on the end-device then queries these embeddings to assess network flow normality. This architecture alleviates the computational burden of running a full transformer on the device while capitalizing on its analytical performance. Our optimized BERT model is reduced by approximately 90% from its original size, now representing approximately 41 MB, suitable for the Edge. The resulting compact neural network is a mere 137 KB, appropriate for the IoT devices. This system achieves 99.9% detection accuracy with an average inference time of under 70 ms on a standard CPU. Finally, the paper discusses the ethical implications of LLM-IoT integration and evaluates the resilience of LLMs in dynamic and adversarial environments. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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29 pages, 1050 KB

Open AccessArticle

A Lightweight Authentication and Key Distribution Protocol for XR Glasses Using PUF and Cloud-Assisted ECC

by Wukjae Cha, Hyang Jin Lee, Sangjin Kook, Keunok Kim and Dongho Won

Sensors 2026, 26(1), 217; https://doi.org/10.3390/s26010217 - 29 Dec 2025

Viewed by 772

Abstract

The rapid convergence of artificial intelligence (AI), cloud computing, and 5G communication has positioned extended reality (XR) as a core technology bridging the physical and virtual worlds. Encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), XR has demonstrated transformative potential across sectors such as healthcare, industry, education, and defense. However, the compact architecture and limited computational capabilities of XR devices render conventional cryptographic authentication schemes inefficient, while the real-time transmission of biometric and positional data introduces significant privacy and security vulnerabilities. To overcome these challenges, this study introduces PXRA (PUF-based XR authentication), a lightweight and secure authentication and key distribution protocol optimized for cloud-assisted XR environments. PXRA utilizes a physically unclonable function (PUF) for device-level hardware authentication and offloads elliptic curve cryptography (ECC) operations to the cloud to enhance computational efficiency. Authenticated encryption with associated data (AEAD) ensures message confidentiality and integrity, while formal verification through ProVerif confirms the protocol’s robustness under the Dolev–Yao adversary model. Experimental results demonstrate that PXRA reduces device-side computational overhead by restricting XR terminals to lightweight PUF and hash functions, achieving an average authentication latency below 15 ms sufficient for real-time XR performance. Formal analysis verifies PXRA’s resistance to replay, impersonation, and key compromise attacks, while preserving user anonymity and session unlinkability. These findings establish the feasibility of integrating hardware-based PUF authentication with cloud-assisted cryptographic computation to enable secure, scalable, and real-time XR systems. The proposed framework lays a foundation for future XR applications in telemedicine, remote collaboration, and immersive education, where both performance and privacy preservation are paramount. Our contribution lies in a hybrid PUF–cloud ECC architecture, context-bound AEAD for session-splicing resistance, and a noise-resilient BCH-based fuzzy extractor supporting up to 15% BER. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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36 pages, 5378 KB

Open AccessArticle

Hydrostatic Water Displacement Sensing for Continuous Biogas Monitoring

by Marek Habara, Jozef Molitoris, Barbora Jankovičová, Jan Rybář and Ján Vachálek

Sensors 2025, 25(23), 7297; https://doi.org/10.3390/s25237297 - 30 Nov 2025

Viewed by 1383

Abstract

Biogas and biomethane represent promising domestic fuels compatible with decarbonization targets at a time when diversification of gas sources is essential due to market volatility and increasing security risks. In laboratory practice, however, biogas production is still frequently assessed manually, which increases measurement uncertainty, limits temporal resolution, and reduces comparability between experimental series. We present an open and low-cost platform for continuous monitoring based on the hydrostatic water-displacement principle, complemented by stabilized process conditions (temperature control at 37 °C with short-term variability of approximately ±0.02 °C), continuous measurement with a 1 Hz sampling rate, and cloud-based data visualization. The methodology builds on a standardized procedure grounded in well-defined pressure–height–volume conversion relationships and transparent signal processing, enabling objective comparison of substrates and experimental setups. Validation experiments confirmed the system’s capability to capture short-term transient phenomena, improve reproducibility among parallel reactors, and maintain long-term measurement stability. Long-duration tests demonstrated short-term scatter of approximately 0.06 mL, minimal drift below 0.15% per 24 h, and an expanded uncertainty of roughly 3.1% at 100 mL. In parallel BMP tests, the continuous method yielded final volumes 5.78% higher than the discrete pressure method, reflecting systematic bias introduced by sparse manual sampling and reactor handling. The basic configuration quantifies the cumulative volume and production rate of biogas and is readily extendable to online gas composition analysis. The proposed solution offers a replicable tool for research and education, reduces costs, supports measurement standardization, and accelerates the optimization and subsequent scale-up of biogas technologies toward pilot-scale and industrial applications. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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23 pages, 999 KB

Open AccessArticle

Decentralized and Network-Aware Task Offloading for Smart Transportation via Blockchain

by Fan Liang

Sensors 2025, 25(17), 5555; https://doi.org/10.3390/s25175555 - 5 Sep 2025

Cited by 1 | Viewed by 1790

Abstract

As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes—including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers—based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5–24.3%, achieves a task success rate of 84.2–88.5%, improves average resource utilization to 88.9–92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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20 pages, 3555 KB

Open AccessArticle

Model of an Open-Source MicroPython Library for GSM NB-IoT

by Antonii Lupandin, Volodymyr Kopieikin, Maksym Khruslov, Iryna Artyshchuk and Ruslan Shevchuk

Sensors 2025, 25(17), 5322; https://doi.org/10.3390/s25175322 - 27 Aug 2025

Viewed by 1546

Abstract

The growing adoption of the Internet of Things (IoT) demands scalable, energy-efficient communication for autonomous devices. Narrowband IoT (NB-IoT), as a low-power wide-area technology, offers reliable connectivity but remains difficult to integrate in MicroPython systems due to the absence of high-level GSM libraries. This paper introduces a modular, object-oriented MicroPython library that abstracts AT command handling, automates network configuration, and supports protocols such as MQTT and Blynk. The architecture features a layered, hardware-agnostic core and device-specific adapters, enhancing portability and extensibility. The library includes structured exception handling and automated retries to improve system reliability. Empirical validation using a Raspberry Pi Pico and SIM7020E module in a typical IoT scenario demonstrated an up to 81% reduction in implementation time. By providing a reusable and extensible framework, this work improves developer productivity, enhances error resilience, and establishes a solid foundation for rapid NB-IoT application development. Future directions include cross-hardware validation and AI-assisted code and test generation. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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29 pages, 3338 KB

Open AccessArticle

AprilTags in Unity: A Local Alternative to Shared Spatial Anchors for Synergistic Shared Space Applications Involving Extended Reality and the Internet of Things

by Amitabh Mishra and Kevin Foster Carff

Sensors 2025, 25(14), 4408; https://doi.org/10.3390/s25144408 - 15 Jul 2025

Viewed by 3844

Abstract

Creating shared spaces is a key part of making extended reality (XR) and Internet of Things (IoT) technology more interactive and collaborative. Currently, one system which stands out in achieving this end commercially involves spatial anchors. Due to the cloud-based nature of these anchors, they can introduce connectivity and privacy issues for projects which need to be isolated from the internet. This research attempts to explore and create a different approach that does not require internet connectivity. This work involves the creation of an AprilTags-based calibration system as a local solution for creating shared XR spaces and investigates its performance. AprilTags are simple, scannable markers that, through computer vision algorithms, can help XR devices figure out position and rotation in a three-dimensional space. This implies that multiple users can be in the same virtual space and in the real-world space at the same time, easily. Our tests in XR showed that this method is accurate and works well for synchronizing multiple users. This approach could make shared XR experiences faster, more private, and easier to use without depending on cloud-based calibration systems. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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31 pages, 2064 KB

Open AccessArticle

2CA-R²: A Hybrid MAC Protocol for Machine-Type Communications

by Sergio Javier-Alvarez, Pablo Hernandez-Duran, Miguel Lopez-Guerrero and Luis Orozco-Barbosa

Sensors 2025, 25(10), 2994; https://doi.org/10.3390/s25102994 - 9 May 2025

Cited by 2 | Viewed by 1125

Abstract

Machine-to-machine (M2M) communications are becoming the most important factor shaping network traffic. However, traditional controls developed for human-generated traffic are not able to cope with new demands. Thus, hybrid MAC protocols have been proposed to make use of the combined advantages of contention and reservation. Most of them are based on a contention stage (where a variant of CSMA/CA or ALOHA is used) followed by a reservation stage (e.g., TDMA or FDMA). In this paper, we introduce 2CA-R², a hybrid MAC protocol for M2M communications intended to be used in the device domain. What distinguishes this proposal is that the contention stage is controlled by a conflict–resolution algorithm known as Adaptive-2C. The protocol was evaluated using a model based on a Markov chain and computer simulations. Its performance was compared with DCF, the MAC technique used in IEEE802.11 standards. Our results show significant improvements over DCF in various metrics of network performance across different traffic situations. We also evaluated the time the protocol takes to resolve an access conflict, and we observed substantial improvements in the number of stations that can be served with the same network resource (in some cases, around a 40% improvement). Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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25 pages, 1515 KB

Open AccessArticle

Lightweight and Efficient Authentication and Key Distribution Scheme for Cloud-Assisted IoT for Telemedicine

by Hyang Jin Lee, Sangjin Kook, Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee and Dongho Won

Sensors 2025, 25(9), 2894; https://doi.org/10.3390/s25092894 - 3 May 2025

Cited by 2 | Viewed by 1435

Abstract

Medical Internet of Things (IoT) systems are crucial in monitoring the health status of patients. Recently, telemedicine services that manage patients remotely by receiving real-time health information from IoT devices attached to or carried by them have experienced significant growth. A primary concern in medical IoT services is ensuring the security of transmitted information and protecting patient privacy. To address these challenges, various authentication schemes have been proposed. We analyze the authentication scheme by Wang et al. and identified several limitations. Specifically, an attacker can exploit information stored in an IoT device to generate an illegitimate session key. Additionally, despite using a cloud center, the scheme lacks efficiency. To overcome these limitations, we propose an authentication and key distribution scheme that incorporates a physically unclonable function (PUF) and public-key computation. To enhance efficiency, computationally intensive public-key operations are performed exclusively in the cloud center. Furthermore, our scheme addresses privacy concerns by employing a temporary ID for IoT devices used to identify patients. We validate the security of our approach using the formal security analysis tool ProVerif. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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31 pages, 4728 KB

Open AccessReview

Trust Attacks and Defense in the Social Internet of Things: Taxonomy and Simulation-Based Evaluation

by Chunying Zhang, Siwu Lan, Liya Wang, Lu Liu and Jing Ren

Sensors 2025, 25(24), 7513; https://doi.org/10.3390/s25247513 - 10 Dec 2025

Cited by 1 | Viewed by 851

Abstract

The Social Internet of Things (SIoT) combines social networks and the Internet of Things, enabling closer interaction between devices, users, and services. However, this interaction brings risks of trust attacks. These trust attacks not only affect the stability of SIoT systems but also threaten personal privacy and data security. This paper provides a decade-long review of SIoT trust attack research. First, it outlines the SIoT architecture, social relationship types, concept of trust, and trust management processes. It maps seven attacks—bad mouthing attack (BMA), ballot stuffing attack (BSA), self-promoting attack (SPA), discriminatory attack (DA), whitewashing attack (WWA), on-off attack (OOA), and opportunistic service attack (OSA)—clarifying their mechanisms and traits. Next, we synthesize the literature on SIoT trust models, enumerate which attack types they address, and classify defense strategies. It then conducts simulation-based comparative experiments on trust attacks to reveal their impact on node trust and transaction processing, compares attack capabilities along disruption speed, attack strength, and stealthiness, and summarizes attack surfaces with corresponding defense recommendations to better guide the design of SIoT trust management schemes. Finally, we identify open challenges and future research directions, to support the development of new trust management models better equipped to address evolving trust attacks. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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32 pages, 2954 KB

Open AccessReview

From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting

by Md Babul Islam, Antonio Guerrieri, Raffaele Gravina, Declan T. Delaney and Giancarlo Fortino

Sensors 2025, 25(22), 6903; https://doi.org/10.3390/s25226903 - 12 Nov 2025

Cited by 2 | Viewed by 2220

Abstract

Smart Agriculture (SA) combines cutting edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and real-time sensing systems with traditional farming practices to enhance productivity, optimize resource use, and support environmental sustainability. A key aspect of SA is the continuous monitoring of field conditions, particularly Soil Moisture (SM), which plays a crucial role in crop growth and water management. Accurate forecasting of SM allows farmers to make timely irrigation decisions, improve field management, and conserve water. To support this, recent studies have increasingly adopted soil sensors, local weather data, and AI-based data-driven models for SM forecasting. In the literature, most existing review articles lack a structured framework and often overlook recent advancements, including privacy-preserving Federated Learning (FL), Transfer Learning (TL), and the integration of Large Language Models (LLMs). To address this gap, this paper proposes a novel taxonomy for SM forecasting and presents a comprehensive review of existing approaches, including traditional machine learning, deep learning, and hybrid models. Using the PRISMA methodology, we reviewed over 189 papers and selected 68 peer-reviewed studies published between 2017 and 2025. These studies are analyzed based on sensor types, input features, AI techniques, data durations, and evaluation metrics. Six guiding research questions were developed to shape the review and inform the taxonomy. Finally, this work identifies promising research directions, such as the application of TinyML for edge deployment, explainable AI for improved transparency, and privacy-aware model training. This review aims to provide researchers and practitioners with valuable insights for building accurate, scalable, and trustworthy SM forecasting systems to advance SA. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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41 pages, 2392 KB

Open AccessReview

How Beyond-5G and 6G Makes IIoT and the Smart Grid Green—A Survey

by Pal Varga, Áron István Jászberényi, Dániel Pásztor, Balazs Nagy, Muhammad Nasar and David Raisz

Sensors 2025, 25(13), 4222; https://doi.org/10.3390/s25134222 - 6 Jul 2025

Cited by 13 | Viewed by 4666

Abstract

The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. It highlights the critical challenges in achieving energy efficiency, interoperability, and real-time responsiveness across different domains. The paper reviews key enablers such as LPWAN, wake-up radios, mobile edge computing, and energy harvesting techniques for green IoT, as well as optimization strategies for 5G/6G networks and data center operations. Furthermore, it examines the role of 5G in enabling reliable, ultra-low-latency data communication for advanced smart grid applications, such as distributed generation, precise load control, and intelligent feeder automation. Through a structured analysis of recent advances and open research problems, the paper aims to identify essential directions for future research and development in building energy-efficient, resilient, and scalable smart infrastructures powered by intelligent wireless networks. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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39 pages, 6737 KB

Open AccessReview

Materials-Driven Advancements in Chipless Radio-Frequency Identification and Antenna Technologies

by Hafsa Anam, Syed Muzahir Abbas, Iain B. Collings and Subhas Mukhopadhyay

Sensors 2025, 25(9), 2867; https://doi.org/10.3390/s25092867 - 1 May 2025

Cited by 5 | Viewed by 2520

Abstract

This article presents a comprehensive analysis of the technical characteristics of advanced versatile materials used in chipless radio-frequency identification (RFID) tags and antennas. The focus is on materials that are used as radiators and substrates. Crucial aspects include flexibility, weight, size, gain, environmental sustainability, efficiency, fabrication time and type, and cost. A comprehensive set of tables are presented that summarize and compare material properties. The materials include flexible high-tech ink substances, graphene, and liquid crystals, as well as metamaterials which possess properties that allow for an increased bandwidth. Printing techniques are discussed for high-performance high-resolution fabricated tags. This paper contributes by systematically comparing emerging materials for chipless RFID tags, highlighting their impact on performance and sustainability. It also provides practical guidance for material selection and fabrication techniques to enable next-generation wireless applications. It presents a broad understanding of various materials and their use. The paper provides direction for the deployment and utilization of inexpensive passive chipless RFID tags in future intelligent wireless networks. The advancement of chipless RFID is largely driven by the development of innovative materials, especially in the realm of advanced materials and smart materials, which enable the creation of more cost-effective, flexible, and scalable RFID systems. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)

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