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Applications of Sensors Based on Embedded Systems
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Applications of Sensors Based on Embedded Systems

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 7697

Special Issue Editors

Dr. Sergio Spanò

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: electronics and communication engineering; digital signal processing; machine learning; FPGA
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Di Nunzio

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: electronics and communication engineering; digital signal processing; machine learning; FPGA
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of sensors applied to the edge, non-functional constraints such as execution time, memory capacity, and energy consumption are a significant challenge for designers of embedded systems. New applications are being proposed that integrate an increasing variety of functionality into everyday objects, imposing several additional requirements on embedded system designers, as follows:

  • Increased computing workloads, elaborating and fusing multiple sensor data, even by advanced machine learning techniques;
  • Reduced power consumption, allowing for smaller batteries and renewable power sources;
  • Faster interaction with the environment, necessitating a high performance in data processing that is often reached by hardware implementations.

As an example, the physical dimensions and power consumption of embedded sensors for the Internet of Things are frequently of interest. However, the need for small systems does not prevent higher demands for functionality and speed. Simultaneously, designers must respond to a growing need for more powerful edge systems capable of managing vast fleets of connected devices while running resource-intensive algorithms such as sensor fusion, feedback control, and machine learning. Developers must grasp the nature of embedded systems architectures and strategies for extracting their full performance potential in this environment, as well as embedded design in general.

This Special Issue invites researchers to contribute original research, case studies, and reviews that address topics related to designs and applications of sensors that are based on embedded systems.

Dr. Sergio Spanò
Dr. Luca Di Nunzio
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

  • applications of sensors to low-power IoT systems
  • FPGA and SoC processing for sensors data
  • microcontrollers processing for sensors data
  • embedded GPU processing for sensors data
  • ASIC processing for sensors data
  • machine learning on the Edge
  • efficient sensors data-processing algorithms

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Published Papers (6 papers)

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Research

21 pages, 2804 KiB  
Article
Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms
by Udhaya Mugil Damodarin, Gian Carlo Cardarilli, Luca Di Nunzio, Marco Re and Sergio Spanò
Sensors 2025, 25(8), 2585; https://doi.org/10.3390/s25082585 - 19 Apr 2025
Viewed by 82
Abstract
This paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured through hardware sensors such [...] Read more.
This paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured through hardware sensors such as current, voltage, and priority indicators, and makes optimal charging decisions to address grid stress and prioritize charging needs. The FPGA implementation leverages hardware design strategies to ensure efficient operation and real-time response within a limited amount of required energy, allowing for its implementation in embedded applications and possibly enabling the use of an energy harvesting power source, like a small solar panel. The proposed design effectively manages multiple EV chargers by dynamically allocating current and prioritizing charging tasks to maintain service quality. Through intelligent decision making, informed by continuous sensor feedback, the system adapts to fluctuating grid conditions and optimizes energy distribution. Key findings highlight the system’s ability to maintain stable operation under varying demand conditions, improving power efficiency, safety, and service reliability. Moreover, the design is scalable, enabling seamless expansion for larger installations by following consistent architectural guidelines. This FPGA-based solution combines RL intelligence, sensor-based environmental perception, and robust hardware design, offering a practical framework for an efficient EV charging infrastructure in modern smart grid environments. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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29 pages, 21708 KiB  
Article
Design, Implementation and Practical Evaluation of an Opportunistic Communications Protocol Based on Bluetooth Mesh and libp2p
by Ángel Niebla-Montero, Iván Froiz-Míguez, Paula Fraga-Lamas and Tiago M. Fernández-Caramés
Sensors 2025, 25(4), 1190; https://doi.org/10.3390/s25041190 - 15 Feb 2025
Viewed by 442
Abstract
The increasing proliferation of Internet of Things (IoT) devices has created a growing need for more efficient communication networks, especially in areas where continuous connectivity is unstable or unavailable. Opportunistic networks have emerged as a possible solution in such scenarios, allowing for intermittent [...] Read more.
The increasing proliferation of Internet of Things (IoT) devices has created a growing need for more efficient communication networks, especially in areas where continuous connectivity is unstable or unavailable. Opportunistic networks have emerged as a possible solution in such scenarios, allowing for intermittent and decentralized data sharing. This article presents a novel communication protocol that uses Bluetooth 5 and the libp2p framework to enable decentralized and opportunistic communications among IoT devices. The protocol provides dynamic peer discovery and decentralized management, resulting in a more flexible and robust IoT network infrastructure. The performance of the proposed architecture was evaluated through experiments in both controlled and industrial scenarios, with a particular emphasis on latency and on the impact of the presence of obstacles. The obtained results show that the protocol has the ability to improve data transfer in environments with limited connectivity, making it adequate for both urban and rural areas, as well as for challenging environments such as shipyards. Moreover, the presented findings conclude that the protocol works well in situations with minimal signal obstruction and short distances, like homes, where average latency values of about 8 s have been achieved with no losses. Furthermore, the protocol can also be used in industrial scenarios, even when metal obstacles increase signal attenuation, and over long distances, where average latency values of about 8.5 s have been obtained together with packet losses of less than 5%. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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21 pages, 2061 KiB  
Article
Hardware Acceleration of Division-Free Quadrature-Based Square Rooting Approach for Near-Lossless Compression of Hyperspectral Images
by Amal Altamimi and Belgacem Ben Youssef
Sensors 2025, 25(4), 1092; https://doi.org/10.3390/s25041092 - 12 Feb 2025
Viewed by 426
Abstract
Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are [...] Read more.
Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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14 pages, 2955 KiB  
Article
Design of a Multi-Node Data Acquisition System for Logging-While-Drilling Acoustic Logging Instruments Based on FPGA
by Zhenyu Qin, Junqiang Lu, Baiyong Men, Shijie Wei and Jiakang Pan
Sensors 2025, 25(3), 808; https://doi.org/10.3390/s25030808 - 29 Jan 2025
Viewed by 597
Abstract
The logging-while-drilling (LWD) acoustic logging instrument is pivotal in unconventional oil and gas exploration, and in providing real-time assessments of subsurface formations. The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals [...] Read more.
The logging-while-drilling (LWD) acoustic logging instrument is pivotal in unconventional oil and gas exploration, and in providing real-time assessments of subsurface formations. The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals from the formation, which directly affects the accuracy and timeliness of the logging data. Recognizing the constraints of current LWD acquisition systems, including limited data collection capabilities and inadequate precision, this study introduces an FPGA-based multi-node data acquisition system for LWD acoustic logging. This system increases sampling density and data accuracy, leading to a more comprehensive collection of formation information. The multi-node acquisition system is composed primarily of a main control circuit board and several acquisition circuit boards, all connected via an RS485 bus. The Field-Programmable Gate Array (FPGA) is utilized to develop the acquisition circuit board’s firmware, offering adjustable control over parameters, such as the AD7380’s operational mode, sampling rate, and depth, facilitating real-time and concurrent acquisition and storage of formation acoustic signals. The main control board communicates with the acquisition boards via the RS485 bus, issuing commands to enable autonomous data collection and transfer from each board, thus enhancing the system’s reliability and scalability. Experimental results confirm the system’s capacity to efficiently capture waveform signals and upload them in real-time, underscoring its dependability and timeliness. The findings suggest that the system is capable of high-speed, real-time acquisition and processing of acoustic signals, offering robust technical support for the continued application of LWD acoustic logging instruments. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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13 pages, 748 KiB  
Article
Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques
by Randa Jabeur and Alaa Alaerjan
Sensors 2024, 24(23), 7849; https://doi.org/10.3390/s24237849 - 8 Dec 2024
Viewed by 1114
Abstract
Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and [...] Read more.
Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and lifespan of each sensor node, thereby improving the overall performance of the WSN. In this study, we propose a method to efficiently monitor radiofrequency electromagnetic fields (RF-EMF) exposure using WSNs. Our approach leverages sensor nodes to provide real-time measurements, ensuring accurate and timely data collection. With the increasing prevalence of wireless communication systems, assessing RF-EMF exposure has become crucial due to public health concerns. Since individuals spend over 70% of their time indoors, it is vital to evaluate indoor RF-EMF exposure. However, this task is complicated by the complex indoor environments, furniture arrangements, temporal variability of exposure, numerous obstructions with unknown dielectric properties, and uncontrolled factors such as people’s movements and the random positioning of furniture and doors. To address these challenges, we employ a sensor network to monitor RF-EMF exposure limits using embedded sensors. By integrating Internet of Things-embedded sensors with advanced modeling techniques, such as kriging, we characterize and model indoor RF-EMF downlink (DL) exposure effectively. Measurements taken in several buildings within a few hundred meters of base stations equipped with multiple cellular antennas (2G, 3G, 4G, and 5G) demonstrate that the kriging technique using the spherical model provides superior RF-EMF prediction compared with the exponential model. Using the spherical model, we constructed a high-resolution coverage map for the entire corridor, showcasing the effectiveness of our approach. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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27 pages, 22106 KiB  
Article
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio
by Ruben Florez, Facundo Palomino-Quispe, Ana Beatriz Alvarez, Roger Jesus Coaquira-Castillo and Julio Cesar Herrera-Levano
Sensors 2024, 24(19), 6261; https://doi.org/10.3390/s24196261 - 27 Sep 2024
Cited by 5 | Viewed by 4170
Abstract
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address [...] Read more.
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning–Microsleep–Eyeblink–Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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