Next-Generation IoT and Smart Systems for Communication and Sensing

Topic Information

Dear Colleagues,

The following Topical Advisory Panel is dedicated to the advancement of next-generation Internet of Things (IoT) architectures and intelligent systems aimed at improving communication, sensing, and decision-making processes. In light of the increasing demand for interconnected devices and smart environments, the panel investigates pioneering strategies in embedded systems, distributed sensing, and communication technologies.

Special emphasis is placed on emerging applications within industrial automation, smart cities, and healthcare monitoring. These domains require robust designs that integrate intelligent decision-making, low-latency communication, and adaptive sensing capabilities.

Low-latency communication is particularly appreciated in smart cities, given the utilization of massive quantities of data, concerning technologies based on cloud computing, big data analytics, and the Internet of Things (IoT), which demands seamless connectivity and real-time data processing.

We therefore cordially invite the submission of scientific articles concerning (but not limited to) the following topics:

• IoT-enabled sensing
• Communication protocols
• Intelligent system architectures
• Embedded technologies and their applications (e.g., smart manufacturing, smart cities, environmental monitoring, healthcare systems, and urban infrastructure development)

Submissions may include original research, case studies, and innovative designs that tackle the challenges and opportunities present in this rapidly evolving domain.

Dr. Luis Pires
Dr. Ricardo Santos
Dr. Francisco Rego
Dr. Vitor Fialho
Dr. Dinh-Thuan Do
Dr. Vasco Velez
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit
IoT IoT	2.8	8.7	2020	25.7 Days	CHF 1400	Submit
Technologies technologies	3.6	8.5	2013	21.8 Days	CHF 1600	Submit
Inventions inventions	1.9	4.9	2016	21.8 Days	CHF 1800	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Vehicles vehicles	2.2	5.3	2019	22.1 Days	CHF 1600	Submit

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Published Papers (6 papers)

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All Applied Sciences Electronics Future Internet IoT Technologies Inventions Sensors Vehicles

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19 pages, 4054 KB  

Open AccessArticle

DSGF-YOLO: A Lightweight Deep Neural Network for Traffic Accident Detection and Severity Classifications

by Weijun Li, Huawei Xie and Peiteng Lin

Vehicles 2025, 7(4), 153; https://doi.org/10.3390/vehicles7040153 - 5 Dec 2025

Viewed by 294

Abstract

Traffic accidents pose unpredictable and severe social and economic challenges. Rapid and accurate accident detection, along with reliable severity classification, is essential for timely emergency response and improved road safety. This study proposes DSGF-YOLO, an enhanced deep learning framework based on the YOLOv13 [...] Read more.

Traffic accidents pose unpredictable and severe social and economic challenges. Rapid and accurate accident detection, along with reliable severity classification, is essential for timely emergency response and improved road safety. This study proposes DSGF-YOLO, an enhanced deep learning framework based on the YOLOv13 architecture, developed for automated road accident detection and severity classification. The proposed methodology integrates two novel components: the DS-C3K2-FasterNet-Block module, which enhances local feature extraction and computational efficiency, and the Grouped Channel-Wise Self-Attention (G-CSA) module, which strengthens global context modeling and small-object perception. Comprehensive experiments on a diverse traffic accident dataset validate the effectiveness of the proposed framework. The results show that DSGF-YOLO achieves higher precision, recall, and mean average precision than state-of-the-art models such as Faster R-CNN, DETR, and other YOLO variants, while maintaining real-time performance. These findings highlight its potential for intelligent transportation systems and real-world accident monitoring applications. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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23 pages, 1517 KB  

Open AccessArticle

Bridging Heterogeneous Agents: A Neuro-Symbolic Knowledge Transfer Approach

by Artem Isakov, Artem Zaglubotskii, Ivan Tomilov, Natalia Gusarova, Aleksandra Vatian and Alexander Boukhanovsky

Technologies 2025, 13(12), 568; https://doi.org/10.3390/technologies13120568 - 4 Dec 2025

Viewed by 391

Abstract

This paper presents a neuro-symbolic approach for constructing distributed knowledge graphs to facilitate cooperation through communication among spatially proximate agents. We develop a graph autoencoder (GAE) that learns rich representations from heterogeneous modalities. The method employs density-adaptive k-nearest neighbor (k-NN) [...] Read more.

This paper presents a neuro-symbolic approach for constructing distributed knowledge graphs to facilitate cooperation through communication among spatially proximate agents. We develop a graph autoencoder (GAE) that learns rich representations from heterogeneous modalities. The method employs density-adaptive k-nearest neighbor (k-NN) construction with Gabriel pruning to build the proximity graphs that balance local density awareness with geometric consistency. When the agents enter the bridging zone, their individual knowledge graphs are aggregated into hypergraphs using a construction algorithm, for which we derive the theoretical bounds on the minimum number of hyperedges required for connectivity under arity and locality constraints. We evaluate the approach in PettingZoo’s communication-oriented environment, observing improvements of approximately 10% in episode rewards and up to 40% in individual agent rewards compared to Deep Q-Network (DQN) baselines, while maintaining comparable policy loss values. The explicit graph structures may offer interpretability benefits for applications requiring auditability. This work explores how structured knowledge representations can support cooperation in distributed multi-agent systems with heterogeneous observations. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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25 pages, 2154 KB  

Open AccessArticle

A Multimodal Polygraph Framework with Optimized Machine Learning for Robust Deception Detection

by Omar Shalash, Ahmed Métwalli, Mohammed Sallam and Esraa Khatab

Inventions 2025, 10(6), 96; https://doi.org/10.3390/inventions10060096 - 29 Oct 2025

Viewed by 1148

Abstract

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the [...] Read more.

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated. Random Forest has achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. With a computation time of 0.06 s, Random Forest has proven to be efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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18 pages, 5353 KB  

Open AccessCommunication

A Reconfigurable Memristor-Based Computing-in-Memory Circuit for Content-Addressable Memory in Sensor Systems

by Hao Hu, Yian Liu, Shuang Liu, Junjie Wang, Siyu Xiao, Shiqin Yan, Ruicheng Pan, Yang Wang, Xingyu Liao, Tianhao Mao, Yutong Chen, Xiangzhan Wang and Yang Liu

Sensors 2025, 25(20), 6464; https://doi.org/10.3390/s25206464 - 19 Oct 2025

Viewed by 1452

Abstract

To meet the demand for energy-efficient and high-performance computing in resource-limited sensor edge applications, this paper presents a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM). The scheme exploits the analog multi-level resistance characteristics of memristors to enable parallel multi-bit processing, overcoming the [...] Read more.

To meet the demand for energy-efficient and high-performance computing in resource-limited sensor edge applications, this paper presents a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM). The scheme exploits the analog multi-level resistance characteristics of memristors to enable parallel multi-bit processing, overcoming the constraints of traditional binary computing and significantly improving storage density and computational efficiency. Furthermore, by employing dynamic adjustment of the mapping between input signals and reference voltages, the circuit supports dynamic switching between exact and approximate CAM modes, substantially enhancing functional flexibility. Experimental results demonstrate that the 32 × 36 memristor array based on a TiN/TiOx/HfO₂/TiN structure exhibits eight stable and distinguishable resistance states with excellent retention characteristics. In large-scale array simulations, the minimum voltage separation between state-representing waveforms exceeds 6.5 mV, ensuring reliable discrimination by the readout circuit. This work provides an efficient and scalable hardware solution for intelligent edge computing in next-generation sensor networks, particularly suitable for real-time biometric recognition, distributed sensor data fusion, and lightweight artificial intelligence inference, effectively reducing system dependence on cloud communication and overall power consumption. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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23 pages, 4379 KB  

Open AccessArticle

Large Vision Language Model: Enhanced-RSCLIP with Exemplar-Image Prompting for Uncommon Object Detection in Satellite Imagery

by Taiwo Efunogbon, Abimbola Efunogbon, Enjie Liu, Dayou Li and Renxi Qiu

Electronics 2025, 14(15), 3071; https://doi.org/10.3390/electronics14153071 - 31 Jul 2025

Viewed by 1519

Abstract

Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in [...] Read more.

Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in satellite imagery. Our approach introduces a key innovation where an exemplar-image preprocessing module using crop-based or attention-based algorithms extracts focused object features which are fed as a dual stream to a contrastive learning framework that fuses textual descriptions with visual exemplar embeddings. We evaluated our method on a custom dataset of 260 satellite images across UK and Nigerian regions. Enhanced-RSCLIP with crop-based exemplar processing achieved 72% accuracy in cattle detection and 56.2% overall accuracy on cross-domain transfer tasks, significantly outperforming text-only CLIP (31% overall accuracy). The dual-prompt architecture enables effective few-shot learning and cross-regional transfer from data-rich (UK) to data-sparse (Nigeria) environments, demonstrating a 41% improvement over baseline approaches for uncommon object detection in satellite imagery. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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36 pages, 2135 KB  

Open AccessArticle

Privacy Framework for the Development of IoT-Based Systems

by Yaqin Y. Shaheen, Miguel J. Hornos and Carlos Rodríguez-Domínguez

Future Internet 2025, 17(8), 322; https://doi.org/10.3390/fi17080322 - 22 Jul 2025

Viewed by 787

Abstract

Addressing privacy concerns is one of the key challenges facing the development of Internet of Things (IoT)-based systems (IoTSs). As IoT devices often collect and process personal and sensitive information, strict privacy policies must be defined and enforced to keep data secure and [...] Read more.

Addressing privacy concerns is one of the key challenges facing the development of Internet of Things (IoT)-based systems (IoTSs). As IoT devices often collect and process personal and sensitive information, strict privacy policies must be defined and enforced to keep data secure and safe, ensuring security and regulatory compliance. Any data breach could compromise the security of the system, leading to various types of threats and attacks, some of which could even endanger human life. Therefore, it is crucial to design and build a comprehensive and general privacy framework for the development of IoTSs. This framework should not be limited to specific IoTS domains but should be general enough to support and cover most IoTS domains. In this paper, we present a framework that assists developers by (i) enabling them to build IoTSs that comply with privacy standards, such as the General Data Protection Regulation (GDPR), and (ii) providing a simplified and practical approach to identifying and addressing privacy concerns. In addition, the framework enables developers to implement effective countermeasures. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

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