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Search Results (2,071)

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37 pages, 1895 KiB  
Review
A Review of Artificial Intelligence and Deep Learning Approaches for Resource Management in Smart Buildings
by Bibars Amangeldy, Timur Imankulov, Nurdaulet Tasmurzayev, Gulmira Dikhanbayeva and Yedil Nurakhov
Buildings 2025, 15(15), 2631; https://doi.org/10.3390/buildings15152631 - 25 Jul 2025
Abstract
This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying [...] Read more.
This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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23 pages, 650 KiB  
Article
Exercise-Specific YANG Profile for AI-Assisted Network Security Labs: Bidirectional Configuration Exchange with Large Language Models
by Yuichiro Tateiwa
Information 2025, 16(8), 631; https://doi.org/10.3390/info16080631 - 24 Jul 2025
Abstract
Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that [...] Read more.
Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article
(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)
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13 pages, 560 KiB  
Article
Balancing Complexity and Performance in Convolutional Neural Network Models for QUIC Traffic Classification
by Giovanni Pettorru, Matteo Flumini and Marco Martalò
Sensors 2025, 25(15), 4576; https://doi.org/10.3390/s25154576 - 24 Jul 2025
Abstract
The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential [...] Read more.
The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article
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26 pages, 4687 KiB  
Article
Comparative Evaluation of YOLO and Gemini AI Models for Road Damage Detection and Mapping
by Zeynep Demirel, Shvan Tahir Nasraldeen, Öykü Pehlivan, Sarmad Shoman, Mustafa Albdairi and Ali Almusawi
Future Transp. 2025, 5(3), 91; https://doi.org/10.3390/futuretransp5030091 - 22 Jul 2025
Viewed by 150
Abstract
Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection [...] Read more.
Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article
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35 pages, 2590 KiB  
Review
Advanced Chemometric Techniques for Environmental Pollution Monitoring and Assessment: A Review
by Shaikh Manirul Haque, Yunusa Umar and Abuzar Kabir
Chemosensors 2025, 13(7), 268; https://doi.org/10.3390/chemosensors13070268 - 21 Jul 2025
Viewed by 196
Abstract
Chemometrics has emerged as a powerful approach for deciphering complex environmental systems, enabling the identification of pollution sources through the integration of faunal community structures with physicochemical parameters and in situ analytical data. Leveraging advanced technologies—including satellite imaging, drone surveillance, sensor networks, and [...] Read more.
Chemometrics has emerged as a powerful approach for deciphering complex environmental systems, enabling the identification of pollution sources through the integration of faunal community structures with physicochemical parameters and in situ analytical data. Leveraging advanced technologies—including satellite imaging, drone surveillance, sensor networks, and Internet of Things platforms—chemometric methods facilitate real-time and longitudinal monitoring of both pristine and anthropogenically influenced ecosystems. This review provides a critical and comprehensive overview of the foundational principles underpinning chemometric applications in environmental science. Emphasis is placed on identifying pollution sources, their ecological distribution, and potential impacts on human health. Furthermore, the study highlights the role of chemometrics in interpreting multidimensional datasets, thereby enhancing the accuracy and efficiency of modern environmental monitoring systems across diverse geographic and industrial contexts. A comparative analysis of analytical techniques, target analytes, application domains, and the strengths and limitations of selected in situ and remote sensing-based chemometric approaches is also presented. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Chemical Detection and Analysis)
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19 pages, 5087 KiB  
Review
Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality
by Gajanan A. Bodkhe, Vishal Kumar, Xingjie Li, Shichun Pei, Long Ma and Myunghee Kim
Microorganisms 2025, 13(7), 1706; https://doi.org/10.3390/microorganisms13071706 - 21 Jul 2025
Viewed by 318
Abstract
Microorganisms play a crucial role in food processes, safety, and quality through their dynamic interactions with other organisms. In recent years, biosensors have become essential tools for monitoring these processes in the dairy, meat, and fresh produce industries. This review highlights how microbial [...] Read more.
Microorganisms play a crucial role in food processes, safety, and quality through their dynamic interactions with other organisms. In recent years, biosensors have become essential tools for monitoring these processes in the dairy, meat, and fresh produce industries. This review highlights how microbial diversity, starter cultures, and interactions, such as competition and quorum sensing, shape food ecosystems. Diverse biosensor platforms, including electrochemical, optical, piezoelectric, thermal, field-effect transistor-based, and lateral flow assays, offer distinct advantages tailored to specific food matrices and microbial targets, enabling rapid and sensitive detection. Biosensors have been developed for detecting pathogens in real-time monitoring of fermentation and tracking spoilage. Control strategies, including bacteriocins, probiotics, and biofilm management, support food safety, while decontamination methods provide an additional layer of protection. The integration of new techniques, such as nanotechnology, CRISPR, and artificial intelligence, into Internet of Things systems is enhancing precision, particularly in addressing regional food safety challenges. However, their adoption is still hindered by complex food matrices, high costs, and the growing challenge of antimicrobial resistance. Looking ahead, intelligent systems and wearable sensors may help overcome these barriers. Although gaps in standardization and accessibility remain, biosensors are well-positioned to revolutionize food microbiology, linking ecological insights to practical solutions and paving the way for safer, high-quality food worldwide. Full article
(This article belongs to the Special Issue Feature Papers in Food Microbiology)
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40 pages, 17591 KiB  
Article
Research and Education in Robotics: A Comprehensive Review, Trends, Challenges, and Future Directions
by Mutaz Ryalat, Natheer Almtireen, Ghaith Al-refai, Hisham Elmoaqet and Nathir Rawashdeh
J. Sens. Actuator Netw. 2025, 14(4), 76; https://doi.org/10.3390/jsan14040076 - 16 Jul 2025
Viewed by 590
Abstract
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution [...] Read more.
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article
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19 pages, 1971 KiB  
Article
IoMT Architecture for Fully Automated Point-of-Care Molecular Diagnostic Device
by Min-Gin Kim, Byeong-Heon Kil, Mun-Ho Ryu and Jong-Dae Kim
Sensors 2025, 25(14), 4426; https://doi.org/10.3390/s25144426 - 16 Jul 2025
Viewed by 322
Abstract
The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory [...] Read more.
The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection. Full article
(This article belongs to the Special Issue Advances in Sensors and IoT for Health Monitoring)
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23 pages, 951 KiB  
Article
Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments
by Panagiotis D. Paraschos, Georgios Papadopoulos and Dimitrios E. Koulouriotis
Machines 2025, 13(7), 611; https://doi.org/10.3390/machines13070611 - 16 Jul 2025
Viewed by 272
Abstract
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data [...] Read more.
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article
(This article belongs to the Section Advanced Manufacturing)
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30 pages, 4318 KiB  
Article
AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region
by Pavlos Nikolaidis
Energies 2025, 18(14), 3731; https://doi.org/10.3390/en18143731 - 15 Jul 2025
Viewed by 158
Abstract
Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, [...] Read more.
Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. A distinguishing feature of this work is the integration of cross-continental dust events and diverse atmospheric parameters into a structured forecasting model. A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. The obtained results demonstrate that incorporating dust deposition and air composition features significantly enhances forecasting accuracy, particularly during severe dust episodes. This research not only fills a notable gap in the PV forecasting literature but also provides a scalable model for other dust-prone regions transitioning to high levels of solar energy integration. Full article
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28 pages, 1536 KiB  
Review
Remote Non-Destructive Testing of Port Cranes: A Review of Vibration and Acoustic Sensors with IoT Integration
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Rafał Grzejda and Farah Syazwani Shahar
J. Mar. Sci. Eng. 2025, 13(7), 1338; https://doi.org/10.3390/jmse13071338 - 13 Jul 2025
Viewed by 470
Abstract
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of [...] Read more.
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations. Full article
(This article belongs to the Section Ocean Engineering)
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26 pages, 3905 KiB  
Article
Data Collection and Remote Control of an IoT Electronic Nose Using Web Services and the MQTT Protocol
by Juan J. Pérez-Solano and Antonio Ruiz-Canales
Sensors 2025, 25(14), 4356; https://doi.org/10.3390/s25144356 - 11 Jul 2025
Viewed by 221
Abstract
An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound [...] Read more.
An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound identification and sample classification. The deployment of such devices often requires the possibility of having remote access over the Internet to manage their operation and to collect the sampled data. In this context, the application of web technologies to the monitoring and supervision of these systems connected to the Internet, which can be considered as an Internet of Things (IoT) device, offers the advantage of not requiring the development of client-side applications. Users can employ a browser to connect to the IoT device and monitor or control its operation. Moreover, web design enables the development of cross-platform web monitoring systems. In addition, the inclusion of the MQTT protocol and the utilization of a virtual private network (VPN) enable a secure transmission and collection of the sampled data. In this work, all these technologies have been applied in the development of a system to manage and collect data to monitor rot in lemons treated with sodium benzoate before harvest. Full article
(This article belongs to the Special Issue Electronic Nose and Artificial Olfaction)
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23 pages, 3874 KiB  
Article
Optimal Media Control Strategy for Rumor Propagation in a Multilingual Dual Layer Reaction Diffusion Network Model
by Guiyun Liu, Haozhe Xu, Yu Zhu, Yiyang Ma and Zhipeng Chen
Mathematics 2025, 13(14), 2253; https://doi.org/10.3390/math13142253 - 11 Jul 2025
Viewed by 159
Abstract
The rapid advancement of Internet of Things technologies has significantly enhanced cross-regional communication among geographically and linguistically diverse populations on social platforms yet simultaneously accelerated the propagation of rumors across multilingual networks at unprecedented velocity. Therefore, this study focuses on investigating the spatiotemporal [...] Read more.
The rapid advancement of Internet of Things technologies has significantly enhanced cross-regional communication among geographically and linguistically diverse populations on social platforms yet simultaneously accelerated the propagation of rumors across multilingual networks at unprecedented velocity. Therefore, this study focuses on investigating the spatiotemporal propagation dynamics and cross-lingual diffusion characteristics of rumors. Distinguished from conventional approaches, we innovatively formulate a hybrid dual-layer rumor containment model through a reaction–diffusion framework that explicitly incorporates the coupling control effects of media layers with independent propagation dynamics. Furthermore, we rigorously prove the differentiability of control-to-state mappings, which enables the derivation of necessary optimality conditions for the optimal control problem. Finally, comprehensive simulations validate both the adaptability and effectiveness of our media-based spatiotemporal control strategies in multilingual environments. Full article
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15 pages, 271 KiB  
Article
Evaluating the Energy Costs of SHA-256 and SHA-3 (KangarooTwelve) in Resource-Constrained IoT Devices
by Iain Baird, Isam Wadhaj, Baraq Ghaleb, Craig Thomson and Gordon Russell
IoT 2025, 6(3), 40; https://doi.org/10.3390/iot6030040 - 11 Jul 2025
Viewed by 284
Abstract
The rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility [...] Read more.
The rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility and post-quantum resilience for lightweight resource-constrained devices. This paper presents a comparative evaluation of the energy costs associated with SHA-256 and SHA-3 hashing in Contiki 3.0, using three generationally distinct IoT platforms: Sky Mote, Z1 Mote, and Wismote. Unlike previous studies that rely on hardware acceleration or limited scope, our work conducts a uniform, software-only analysis across all motes, employing consistent radio duty cycling, ContikiMAC (a low-power Medium Access Control protocol) and isolating the cryptographic workload from network overhead. The empirical results from the Cooja simulator reveal that while SHA-3 provides advanced security features, it incurs significantly higher CPU and, in some cases, radio energy costs particularly on legacy hardware. However, modern platforms like Wismote demonstrate a more balanced trade-off, making SHA-3 viable in higher-capability deployments. These findings offer actionable guidance for designers of secure IoT systems, highlighting the practical implications of cryptographic selection in energy-sensitive environments. Full article
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27 pages, 1665 KiB  
Article
A Heuristic Optical Flow Scheduling Algorithm for Low-Delay Vehicular Visible Light Communication
by Zhengying Cai, Shumeng Lei, Jingyi Li, Chen Yu, Junyu Liu and Guoqiang Gong
Photonics 2025, 12(7), 693; https://doi.org/10.3390/photonics12070693 - 9 Jul 2025
Viewed by 172
Abstract
Vehicular visible light communication (VVLC) with ultralow electromagnetic interference has great potential to propel the growth of the Internet of Vehicles (IoV). However, ensuring quick response times and minimal delays in VVLC is a significant challenge brought on by fast-moving vehicles. In response [...] Read more.
Vehicular visible light communication (VVLC) with ultralow electromagnetic interference has great potential to propel the growth of the Internet of Vehicles (IoV). However, ensuring quick response times and minimal delays in VVLC is a significant challenge brought on by fast-moving vehicles. In response to this problem, we propose a heuristic optical flow scheduling algorithm. First, the optical flow scheduling problem of VVLC is built as a multi-objective optimization model considering the makespan, delay, schedulable ratio, and bandwidth utilization with non-conflict constraints. Second, an improved artificial plant community (APC) algorithm with enhanced global and local search capabilities is proposed to achieve low-delay communication for time-sensitive optical flows. Finally, a series of benchmark experiments are conducted to show that the proposed algorithm can efficiently schedule optical flows with minimal delay. The cost of this algorithm is very low, and it is suitable for deployment on edge computing platforms such as vehicles. Full article
(This article belongs to the Special Issue New Advances in Optical Wireless Communication)
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