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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 62795

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Special Issue Editor

Prof. Dr. Mehmet Rasit Yuce

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Guest Editor

Department of Electrical and Computer Systems, Monash University, Clayton, VIC 3800, Australia
Interests: wearable devices; IoT sensors; bioelectronics; IC circuits; wireless body area networks; MEMs design; biomedial circuits; RF electronics; energy harvesting; sensor/sensor interface circuits and low-power circuits for emerging technologies in wireless communications, such as UWB technology and the Internet of Things (IoT)
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Special Issue Information

Dear Colleagues,

This Special Issue “Feature Review Papers in Intelligent Sensors” aims to collect high-quality review papers in the research fields of interest related to smart and intelligent sensors. (https://www.mdpi.com/journal/sensors/sections/Intelligent_Sensors).

We welcome all researchers from the following fields to contribute review papers highlighting the latest developments in their research field or to invite relevant experts and colleagues to do so.

Sensor Signal Processing;
Computer Vision;
Intelligent Image/Visual Sensors;
Intelligent Integrated Sensors;
Integrated Circuit;
Human–Robot/Machine/Computer Interaction;
Artificial Intelligence;
Intelligent Instrumentation;
Intelligent Portable Platforms;
Intelligent Computing;
Intelligent Tactile Sensors;
Wireless Sensor Network (WSN);
Smart Sensor Network;
Intelligent Acoustic/Ultrasonic Sensors;
Intelligent Environmental Monitoring;
Smart Cities;
Smart Home/Home Automation;
Smart Sensing Manufacturing and Industry;
Smart Energy Management/Smart Grids;
Smart Agriculture;
Smart Health Monitoring;
E-Health/M-Health;
Wireless Body Area Networks/Wireless Body Sensor Networks;
Intelligent Emotion Recognition;
Smart Building/Smart Civil Infrastructure;
Smart/Precision Farming;
Blockchain 5G/6G.

Prof. Dr. Mehmet Rasit Yuce
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. 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

smart sensors
intelligent sensors
sensor network
sensor data
sensor fusion
sensor applications
artificial intelligence

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Published Papers (10 papers)

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Review

43 pages, 2428 KiB

Open AccessReview

A Survey on Directed Acyclic Graph-Based Blockchain in Smart Mobility

by Yuhao Bai, Soojin Lee and Seung-Hyun Seo

Sensors 2025, 25(4), 1108; https://doi.org/10.3390/s25041108 - 12 Feb 2025

Viewed by 925

Abstract

This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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68 pages, 11118 KiB

Open AccessReview

A Review of Simultaneous Localization and Mapping for the Robotic-Based Nondestructive Evaluation of Infrastructures

by Ali Ghadimzadeh Alamdari, Farzad Azizi Zade and Arvin Ebrahimkhanlou

Sensors 2025, 25(3), 712; https://doi.org/10.3390/s25030712 - 24 Jan 2025

Cited by 1 | Viewed by 2200

Abstract

The maturity of simultaneous localization and mapping (SLAM) methods has now reached a significant level that motivates in-depth and problem-specific reviews. The focus of this study is to investigate the evolution of vision-based, LiDAR-based, and a combination of these methods and evaluate their performance in enclosed and GPS-denied (EGD) conditions for infrastructure inspection. This paper categorizes and analyzes the SLAM methods in detail, considering the sensor fusion type and chronological order. The paper analyzes the performance of eleven open-source SLAM solutions, containing two visual (VINS-Mono, ORB-SLAM 2), eight LiDAR-based (LIO-SAM, Fast-LIO 2, SC-Fast-LIO 2, LeGO-LOAM, SC-LeGO-LOAM A-LOAM, LINS, F-LOAM) and one combination of the LiDAR and vision-based method (LVI-SAM). The benchmarking section analyzes accuracy and computational resource consumption using our collected dataset and a test dataset. According to the results, LiDAR-based methods performed well under EGD conditions. Contrary to common presumptions, some vision-based methods demonstrate acceptable performance in EGD environments. Additionally, combining vision-based techniques with LiDAR-based methods demonstrates superior performance compared to either vision-based or LiDAR-based methods individually. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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34 pages, 1229 KiB

Open AccessReview

A Review of CNN Applications in Smart Agriculture Using Multimodal Data

by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari and Josiane Mothe

Sensors 2025, 25(2), 472; https://doi.org/10.3390/s25020472 - 15 Jan 2025

Cited by 4 | Viewed by 3834

Abstract

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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31 pages, 1632 KiB

Open AccessReview

Recent Advancements in Localization Technologies for Wireless Capsule Endoscopy: A Technical Review

by Muhammad A. Ali, Neil Tom, Fahad N. Alsunaydih and Mehmet R. Yuce

Sensors 2025, 25(1), 253; https://doi.org/10.3390/s25010253 - 4 Jan 2025

Viewed by 1574

Abstract

Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body’s internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule’s movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule’s trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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12 pages, 653 KiB

Open AccessReview

Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management

by Nuno Mateus, Eduardo Abade, Diogo Coutinho, Miguel-Ángel Gómez, Carlos Lago Peñas and Jaime Sampaio

Sensors 2025, 25(1), 139; https://doi.org/10.3390/s25010139 - 29 Dec 2024

Cited by 3 | Viewed by 5840

Abstract

Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management. Practical examples illustrate how AI applications have significantly advanced each area and how they support and enhance the effectiveness of sports scientists. This manuscript also underscores the importance of ensuring that AI technologies are context-specific and communicated transparently. Additionally, it calls for academic institutions to update their curriculums with AI-focused education, preparing future sports professionals to fully harness its potential. Finally, the manuscript addresses future challenges, such as the unpredictable nature of team sports, emphasizing the need for interdisciplinary collaboration, including clear communication and mutual understanding between sports scientists and AI experts, and the critical balance between AI-driven insights and human expertise. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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38 pages, 3275 KiB

Open AccessReview

Comprehensive Review: High-Performance Positioning Systems for Navigation and Wayfinding for Visually Impaired People

by Jean Marc Feghali, Cheng Feng, Arnab Majumdar and Washington Yotto Ochieng

Sensors 2024, 24(21), 7020; https://doi.org/10.3390/s24217020 - 31 Oct 2024

Viewed by 2493

Abstract

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers’ access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs’ navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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36 pages, 33066 KiB

Open AccessReview

Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

by Jianzhu Huai, Yuxin Shao, Grzegorz Jozkow, Binliang Wang, Dezhong Chen, Yijia He and Alper Yilmaz

Sensors 2024, 24(20), 6595; https://doi.org/10.3390/s24206595 - 13 Oct 2024

Cited by 2 | Viewed by 2020

Abstract

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers the camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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Graphical abstract

41 pages, 19488 KiB

Open AccessReview

Compatibility Review for Object Detection Enhancement through Super-Resolution

by Daehee Kim, Sungmin Lee, Junghyeon Seo, Song Noh and Jaekoo Lee

Sensors 2024, 24(11), 3335; https://doi.org/10.3390/s24113335 - 23 May 2024

Cited by 1 | Viewed by 1533

Abstract

With the introduction of deep learning, a significant amount of research has been conducted in the field of computer vision in the past decade. In particular, research on object detection (OD) continues to progress rapidly. However, despite these advances, some limitations need to be overcome to enable real-world applications of deep learning-based OD models. One such limitation is inaccurate OD when image quality is poor or a target object is small. The performance degradation phenomenon for small objects is similar to the fundamental limitations of an OD model, such as the constraint of the receptive field, which is a difficult problem to solve using only an OD model. Therefore, OD performance can be hindered by low image quality or small target objects. To address this issue, this study investigates the compatibility of super-resolution (SR) and OD techniques to improve detection, particularly for small objects. We analyze the combination of SR and OD models, classifying them based on architectural characteristics. The experimental results show a substantial improvement when integrating OD detectors with SR models. Overall, it was demonstrated that, when the evaluation metrics (PSNR, SSIM) of the SR models are high, the performance in OD is correspondingly high as well. Especially, evaluations on the MS COCO dataset reveal that the enhancement rate for small objects is 9.4% higher compared to all objects. This work provides an analysis of SR and OD model compatibility, demonstrating the potential benefits of their synergistic combination. The experimental code can be found on our GitHub repository. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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32 pages, 4569 KiB

Open AccessReview

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

by Yudan Wang, Jue Li, Xinqiang Zhang, Yongsheng Yao and Yi Peng

Sensors 2024, 24(9), 2777; https://doi.org/10.3390/s24092777 - 26 Apr 2024

Cited by 1 | Viewed by 4380

Abstract

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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32 pages, 2237 KiB

Open AccessEditor’s ChoiceReview

Smart Sensors and Smart Data for Precision Agriculture: A Review

by Abdellatif Soussi, Enrico Zero, Roberto Sacile, Daniele Trinchero and Marco Fossa

Sensors 2024, 24(8), 2647; https://doi.org/10.3390/s24082647 - 21 Apr 2024

Cited by 65 | Viewed by 36799

Abstract

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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