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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 (1 April 2026) | Viewed by 11209

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Special Issue Editors

Dr. Ning Chen

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Guest Editor

Department of Computer Science, City University of Hong Kong, Hong Kong
Interests: Internet of Things; Intelligent Networking; Robustness Optimization

Dr. Zhengjie Yang

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Guest Editor Assistant

School of Computer Science, Faculty of Engineering, The University of Sydney, Australia
Interests: Federated Learing; Edge Computing; Internet of Things

Dr. Songwei Zhang

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Guest Editor

School of Computer Science and Engineering, Northeastern University, Shenyang, China
Interests: internet of things; complex; quantum computing
Special Issues, Collections and Topics in MDPI journals

Dr. Bo Qian

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Guest Editor

Information Systems Architecture Science Research Division, National Institute of Informatics, Tokyo, Japan
Interests: B5G/6G networks; network economics; VANET; resource management; game theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is significant to the current and future generation of information, network, and communication development and applications, and AI-empowered IoT is an emerging research field that can potentially transform both our understanding of fundamental computer science principles and our standard of living. The IoT is increasingly being employed in various areas, rendering“everything smart”, such as smart homes, smart cities, intelligent transportation, environment monitoring, security systems, and advanced manufacturing. Moreover, it is essential to analyze and learn from an abundance of generated data quickly. Current approaches for big data analytics require the full transfer of data to a platform with high computational power, such as the cloud. However, given the projected rise in the number of devices and the resulting data generation rate, this is not feasible. Numerous other problems exist, such as AI-built security issues, cloud attacks, and botnet problems. The International Conference on Smart Internet of Things (SmartIoT) and the International Conference on Information Science and Technology (ICIST) address these challenges.

This Special Issue is dedicated to publishing original research papers that propose new methodologies and research directions and discuss approaches or schemes to tackle current existing issues. Its focus is on innovations that use devices and computing power within the Internet of Things network itself to perform data analysis in a scalable and reliable fashion.

Papers from the SmartIoT2025 conference are particularly welcome.

Topics of interests include, but are not limited to, the following:

IoT sensing, monitoring, networking and routing;
Big data analysis and cloud computing;
Edge computing/fog computing;
Smart cities, intelligent transportation and internet of vehicles;
Artificial intelligence, machine learning and evolutionary computing;
Social networks, multimedia and mobile computing;
Blockchain and emerging research or technologies;
Industrial 4.0 and industrial IoT;
Security and privacy for smart IoT or CPS;
Control and decision making for smart IoT or CPS.

Dr. Ning Chen
Dr. Zhengjie Yang
Dr. Songwei Zhang
Dr. Bo Qian
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

IoT sensing
edge computing/fog computing
smart IoT

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Published Papers (6 papers)

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Research

24 pages, 1936 KB

Open AccessArticle

Zero Trust for NHIs Based on Robust Identity and Access Management for a Resilient IoT Future

by Sthembile Mthethwa, Moses T. Dlamini and Edgar Jembere

Sensors 2026, 26(8), 2392; https://doi.org/10.3390/s26082392 - 14 Apr 2026

Viewed by 743

Abstract

The pervasive adoption of Internet of Things (IoT) devices has profoundly reshaped digital connectivity by enabling real-time data exchange and autonomous interactions on a global scale. While this transformation presents substantial operational benefits, it simultaneously introduces significant security challenges, especially in terms of Identity and Access Management (IAM) for non-human entities, such as sensors, devices, machine agents, and service accounts. Historically, traditional perimeter-based security models, which depend on static trust boundaries and implicit trust for internal actors, have been applied to human identities. However, these models prove inadequate for managing non-human identities. This inadequacy has spurred interest in Zero Trust Architecture (ZTA), an advanced security paradigm based on the principle of “never trust, always verify.” This paper examines the application of ZTA in safeguarding IoT ecosystems, with a particular emphasis on managing non-human identities. The study delves into ZTA’s fundamental principles, such as least privilege, micro-segmentation, continuous monitoring, and identity-centric access control, and evaluates their effective implementation in resource-constrained IoT settings. The research identifies critical implementation challenges and considerations for applying identity-based ZTA within IoT contexts. The findings of this paper underscore that ZTA, when meticulously implemented, provides a robust framework for mitigating the cyber risks inherent in IoT ecosystems. Furthermore, the paper delineates prospective research avenues aimed at integrating ZTA into IoT environments. Ultimately, this study contributes to the expanding body of scholarly knowledge by endorsing Zero Trust as a foundational strategy for contemporary IoT security. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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20 pages, 15353 KB

Open AccessArticle

CDO-POSE: A Lightweight Model for 2D Human Pose Estimation

by Haifeng Xu, Jingke Chen, Shuhan Cai and Jiangling Guo

Sensors 2026, 26(7), 2159; https://doi.org/10.3390/s26072159 - 31 Mar 2026

Viewed by 882

Abstract

Human pose estimation (HPE) aims to localize human keypoints from visual inputs, which faces persistent challenges in balancing high accuracy with computational efficiency in resource constrained and real-time scenarios. To address these challenges, we propose a lightweight method named CDO-POSE based on an improved YOLOv11. Specifically, we first introduce the Context Anchor Attention (CAA) module, which is composed of three convolutional layers and two bottleneck modules to enhance feature representation while maintaining parameter efficiency. Building on this, to address the limited precision of traditional nearest-neighbor upsampling, we incorporate the Dynamic Sampling (DySample) method, which adaptively adjusts the sampling strategy according to feature importance, thereby improving upsampling accuracy. Furthermore, to align the training objective more closely with the goal of precise pose estimation, we employ the Object Keypoint Similarity Loss (OKS-Loss), which provides a more discriminative evaluation of keypoint localization errors. The experiments on MS COCO2017 and CrowdPose datasets demonstrate that our model achieves almost the same accuracy as YOLOv11s-pose with significantly fewer parameters. Moreover, the model achieves 39.79 FPS and 29.23 FPS for inference at 480p and 720p, respectively, on the NVIDIA Jetson Orin Nano, suggesting that it is suitable for real-time deployment on edge devices. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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19 pages, 18266 KB

Open AccessArticle

GECO: A Real-Time Computer Vision-Assisted Gesture Controller for Advanced IoT Home System

by Murilo C. Lopes, Paula A. Silva, Ludwing Marenco, Evandro C. Vilas Boas, João G. A. de Carvalho, Cristiane A. Ferreira, André L. O. Carvalho, Cristiani V. R. Guimarães, Guilherme P. Aquino and Felipe A. P. de Figueiredo

Sensors 2026, 26(1), 61; https://doi.org/10.3390/s26010061 - 21 Dec 2025

Viewed by 1377

Abstract

This paper introduces GECO, a real-time, computer vision-assisted gesture controller for IoT-based smart home systems. The platform uses a markerless MediaPipe interface that combines gesture-driven navigation and command execution, enabling intuitive control of multiple domestic devices. The system integrates binary and analog gestures, such as continuous light dimming based on thumb–index angles, while operating on-device through a private MQTT network. Technical evaluations across multiple Android devices have demonstrated ultra-low latency times (<50 ms), enabling real-time responsiveness. A user experience study with seventeen participants reported high intuitiveness (9.5/10), gesture accuracy (9.2/10), and perceived inclusivity, mainly for individuals with speech impairments and low technological literacy. These results position GECO as a lightweight, accessible, and privacy-preserving interaction framework, advancing the integration of artificial intelligence and IoT within smart home environments. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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23 pages, 3125 KB

Open AccessArticle

Hybrid AI Intrusion Detection: Balancing Accuracy and Efficiency

by Vandit R Joshi, Kwame Assa-Agyei, Tawfik Al-Hadhrami and Sultan Noman Qasem

Sensors 2025, 25(24), 7564; https://doi.org/10.3390/s25247564 - 12 Dec 2025

Cited by 1 | Viewed by 1687

Abstract

The Internet of Things (IoT) has transformed industries, healthcare, and smart environments, but introduces severe security threats due to resource constraints, weak protocols, and heterogeneous infrastructures. Traditional Intrusion Detection Systems (IDS) fail to address critical challenges including scalability across billions of devices, interoperability among diverse protocols, real-time responsiveness under strict latency, data privacy in distributed edge networks, and high false positives in imbalanced traffic. This study provides a systematic comparative evaluation of three representative AI models, CNN-BiLSTM, Random Forest, and XGBoost for IoT intrusion detection on the NSL-KDD and UNSW-NB15 datasets. The analysis quantifies the achievable detection performance and inference latency of each approach, revealing a clear accuracy–latency trade-off that can guide practical model selection: CNN-BiLSTM offers the highest detection capability (F1 up to 0.986) at the cost of higher computational overhead, whereas XGBoost and Random Forest deliver competitive accuracy with significantly lower inference latency (sub-millisecond on conventional hardware). These empirical insights support informed deployment decisions in heterogeneous IoT environments where accuracy-critical gateways and latency-critical sensors coexist. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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24 pages, 2664 KB

Open AccessArticle

AIoT-Based Eyelash Extension Durability Evaluation Using LabVIEW Data Analysis

by Sumei Chiang, Shao-Hsun Chang, Kai-Chao Yao, Po-Yu Kuo and Chien-Tai Hsu

Sensors 2025, 25(16), 5057; https://doi.org/10.3390/s25165057 - 14 Aug 2025

Viewed by 1278

Abstract

This study introduces a novel platform, the Artificial Intelligence of Things Experimental Device Platform (AIoTEDP), to evaluate the durability of eyelash extensions under various environmental factors, including temperature, wind speed, and compression frequency. The experiment employs a three-factor full factorial design, utilizing LabVIEW to collect and analyze independent variables. The retention rate of eyelash extensions is the dependent variable for evaluating the durability. The proposed AIoTEDP regulates thermostats, stepper motors, and heating fans to simulate real-world eyelash extension usage conditions. Quantitative analyses are performed through visual assessments and image recognition technologies. The experimental results indicate that high temperatures and strong winds significantly reduce the durability of eyelash extensions. However, moderate bending damage (3000 repetitions) still allows for sufficient retention. This study validates the practicality and accuracy of the proposed AIoTEDP, showcasing its potential for innovative cosmetic testing systems to assess eyelash extension durability. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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31 pages, 14480 KB

Open AccessArticle

Vega: LLM-Driven Intelligent Chatbot Platform for Internet of Things Control and Development

by Harith Al-Safi, Harith Ibrahim and Paul Steenson

Sensors 2025, 25(12), 3809; https://doi.org/10.3390/s25123809 - 18 Jun 2025

Cited by 7 | Viewed by 4384

Abstract

Large language models (LLMs) have revolutionized natural language processing (NLP), yet their potential in Internet of Things (IoT) and embedded systems (ESys) applications remains largely unexplored. Traditional IoT interfaces often require specialized knowledge, creating barriers for non-technical users. We present Vega, a modular system that leverages LLMs to enable intuitive, natural language control and interrogation of IoT devices, specifically, a Raspberry Pi (RPi) connected to various sensors, actuators, and devices. Our solution comprises three key components: a physical circuit with input and output devices used to showcase the LLM’s ability to interact with hardware, an RPi integrating a control server, and a web application integrating LLM logic. Users interact with the system through natural language, which the LLM interprets to remotely call appropriate commands for the RPi. The RPi executes these instructions on the physically connected circuit, with outcomes communicated back to the user via LLM-generated responses. The system’s performance is empirically evaluated using a range of task complexities and user scenarios, demonstrating its ability to handle complex and conditional logic without additional coding on the RPi, reducing the need for extensive programming on IoT devices. We showcase the system’s real-world applicability through physical circuit implementation while providing insights into its limitations and potential scalability. Our findings reveal that LLM-driven IoT control can effectively bridge the gap between complex device functionality and user-friendly interaction, and also opens new avenues for creative and intelligent IoT applications. This research offers insights into the design and implementation of LLM-integrated IoT interfaces. Full article

(This article belongs to the Special Issue AI-Empowered Internet of Things)

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