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The rapid proliferation of heterogeneous Internet of Things (IoT) devices has introduced a wide range of operational and security challenges, particularly in the domains of device identification and behavior profiling. Traditional fingerprinting methods, which rely primarily on time domain features, often fail to capture the complex, periodic, and often bursty nature of IoT communication—especially in environments characterized by sparse, irregular, or noisy traffic patterns. To address these limitations, two novel frequency-based fingerprinting techniques have been proposed: Spectral-Only Frequency Fingerprint (SFF) and Spectro-Correlative Frequency Fingerprint (SCFF). These approaches shift the analysis from the time domain to the frequency domain, enabling the extraction of richer and more robust behavioral signatures from network traffic. While SFF focuses on capturing the core spectral features of device traffic, SCFF extends this by incorporating inter-feature correlations, offering a more nuanced and comprehensive representation of device behavior. The effectiveness of SFF and SCFF is evaluated across multiple publicly available IoT datasets using a range of machine learning classifiers. Experimental results demonstrate that both fingerprinting methods significantly outperform traditional time domain approaches in terms of accuracy, precision, recall, and F1-score—across all tested classifiers and datasets.

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT’s device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution’s core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture’s optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world.

Data drift caused due to network changes, new device additions, or model degradation alters the patterns learned by ML/DL models, resulting in poor classification performance. This creates the need for a generalized, drift-resilient model that can learn without retraining in dynamic environments. To maintain high accuracy, such a model must classify previously unseen IoT devices effectively. In this study, we propose a three-tier incremental architecture (CNN-PN-RF) combining Convolutional Neural Network (CNN) for feature extraction, Prototypical Network (PN) for class embedding, and Random Forest (RF) for robust classification. The model utilizes six aggregated diverse IoT datasets.Two similarly structured datasets (Dataset 1 and Dataset 2) were created from it, differing in training-testing splits, with some device CSV files withheld to test on unseen classification. Phase 1 employs a stand-alone CNN-based model with L2 regularization, dropout, and early stopping, achieving 70.96% accuracy. Phase 2 integrates CNN with RF, using SMOTE for class balancing and PCA for dimensionality reduction, attaining 83.79% accuracy. Phase 3 introduces PN to finalize the CNN-PN-RF model, enhancing classification issue of feature clustering, intra-class separability, and small-class support. Final accuracy, precision, recall, and F1-score were 99.56%, 99.66%, 99.56%, and 99.59% for Dataset 1, and 99.80% for all metrics on Dataset 2. The model was compared with state-of-the-art approaches and validated on unseen IoT subsets of both datasets, showing better generalization capability.

With the advent of IoT technology in education, understanding its impact on physical education is crucial. This study investigates how the acceptance of wearable IoT devices influences the physical education results of college freshmen. It posits that user acceptance plays a mediating role in the effectiveness of these devices in enhancing physical performance metrics. The study enrolled 150 first-year students from Guangdong University of Finance & Economics, divided equally into an experimental group and a control group. Participants in the experimental group were provided with ‘Xiaomi 8’ smart bracelets to be worn during physical education classes. The study spanned six weeks, focusing on assessing various physical performance metrics and the acceptance of the wearable technology. The data analysis involved comparing the physical performance of both groups and conducting regression analyses to evaluate the mediation effect of acceptance. Results indicated significant improvements in physical performance metrics in the experimental group, as evidenced by the Standardized Mean Differences (SMD). Notably, enhancements were observed in short-distance speed and aerobic endurance. The direct impact of wearable IoT devices on physical performance accounted for 66.4% variance, which increased to 84.1% upon incorporating acceptance as a mediator. These findings suggest that the effectiveness of wearable technology in physical education is significantly influenced by students’ acceptance. The study concludes that wearable IoT devices can effectively enhance physical education outcomes among college students, with user acceptance playing a crucial mediating role. This underscores the importance of considering user acceptance in the integration of technology in educational settings. The findings provide valuable insights for educators and technologists in designing and implementing technology-integrated curricula.

According to recent research, with the ever-increasing use of Internet of Things ( IoT ) devices, there has arisen an ever-growing need for high-performance yet low-power circuits that can efficiently process information. Quantum-dot Cellular Automata ( QCA ) has emerged as a promising alternative to conventional complementary metal-oxide-semiconductor ( CMOS ) technology due to its great potential in digital design at nanoscale levels on account of very low power consumption and very high processing speed. However, QCA circuits are inherently prone to faults due to variations in manufacturing processes and due to the influence of environmental factors. These faults degrade the performance of a QCA circuit considerably. Hence, fault tolerance is one of the major factors of consideration while designing a QCA circuit, particularly when the application requires very reliable and continuous operation, say in an IoT system. As such, this work presents a fault tolerant Carry Skip Adder ( CSA ) for QCA-based circuits. The fault tolerance of basic arithmetic components of IoT nodes performing tasks corresponding to the signal processing, control, and data manipulations is enhanced in the proposed architecture. The area occupied by a fault-tolerant full-adder circuit is 0.06 μm² and a clock cycle is 0.75; its core will be used in the CSA design. It realizes fault-tolerant multiplexers ( MUX ) and a majority gate, which gives the same result when there is a missing or extra single-cell fault. The most astonishing characteristic of this transistor-based CSA is its 85% tolerance for different types of failures. The CSA with three layers contains 1542 quantum cells, 4.75 clock phases, and occupies an area of 4.59 μm². It is compact and efficient architecture; therefore, it is very suitable for IoT applications where the area constraint and power efficiency are the key issues. The proposed CSA will increase the robustness and reliability of QCA-based digital circuits by integrating fault tolerance into its design such that the circuitry based on QCA can keep their functionality on even in fault-prone environments.

The increasing integration of the Internet of Things (IoT) into daily life has led to significant changes in our social interactions. The advent of innovative IoT solutions, combined with the enhanced capabilities and expanded reach of 5G wireless networks, is altering the way humans interact with machines. Notably, the advancement of edge computing, underpinned by 5G networks within IoT frameworks, has markedly extended human sensory perception and interaction. A key biometric within these IoT applications is electroencephalography (EEG), recognized for its sensitivity, cost-effectiveness, and distinctiveness. Traditionally linked to brain–computer interface (BCI) applications, EEG is now finding applications in a wider array of fields, from neuroscience research to the emerging area of neuromarketing. The primary aim of this article is to offer a comprehensive review of the current challenges and future directions in EEG data acquisition, processing, and classification, with a particular focus on the increasing reliance on data-driven methods in the realm of 5G wireless network-supported EEG-enabled IoT solutions. Additionally, the article presents a case study on EEG-based emotion recognition, exemplifying EEG’s role as a biometric tool in the IoT domain, propelled by 5G technology.

Roll-to-roll (R2R) printing methods are well known as additive, cost-effective, and ecologically friendly mass-production methods for processing functional materials and fabricating devices. However, implementing R2R printing to fabricate sophisticated devices is challenging because of the efficiency of material processing, the alignment, and the vulnerability of the polymeric substrate during printing. Therefore, this study proposes the fabrication process of a hybrid device to solve the problems. The device was created so that four layers, composed of polymer insulating layers and conductive circuit layers, are entirely screen-printed layer by layer onto a roll of polyethylene terephthalate (PET) film to produce the circuit. Registration control methods were presented to deal with the PET substrate during printing, and then solid-state components and sensors were assembled and soldered to the printed circuits of the completed devices. In this way, the quality of the devices could be ensured, and the devices could be massively used for specific purposes. Specifically, a hybrid device for personal environmental monitoring was fabricated in this study. The importance of environmental challenges to human welfare and sustainable development is growing. As a result, environmental monitoring is essential to protect public health and serve as a basis for policymaking. In addition to the fabrication of the monitoring devices, a whole monitoring system was also developed to collect and process the data. Here, the monitored data from the fabricated device were personally collected via a mobile phone and uploaded to a cloud server for additional processing. The information could then be utilized for local or global monitoring purposes, moving one step toward creating tools for big data analysis and forecasting. The successful deployment of this system could be a foundation for creating and developing systems for other prospective uses.

In recent years, more and more applied industries have relied on data collection by IoT devices. Various IoT devices generate vast volumes of data that require efficient processing. Usually, the intellectual analysis of such data takes place in data centers in cloud environments. However, the problems of transferring large volumes of data and the long wait for a response from the data center for further corrective actions in the system led to the search for new processing methods. One possible option is Edge computing. Intelligent data analysis in the places of their collection eliminates the disadvantages mentioned above, revealing many advantages of using such an approach in practice. However, the Edge computing approach is challenging to implement when different IoT devices collect the independent attributes required for classification/regression. In order to overcome this limitation, the authors developed a new cascade ensemble-learning model for the deployment at the Edge. It is based on the principles of cascading machine learning methods, where each IoT device that collects data performs its analysis based on the attributes it contains. The results of its work are transmitted to the next IoT device, which analyzes the attributes it collects, taking into account the output of the previous device. All independent at-tributes are taken into account in this way. Because of this, the proposed approach provides: 1) The possibility of effective implementation of Edge computing for intelligent data analysis, that is, even before their transmission to the data center; 2) increasing, and in some cases maintaining, classification/regression accuracy at the same level that can be achieved in the data center; 3) significantly reducing the duration of training procedures due to the processing of a smaller number of attributes by each of the IoT devices. The simulation of the proposed approach was performed on a real-world set of IoT data. The missing data recovery task in the atmospheric air state data was solved. The authors selected the optimal parameters of the proposed approach. It was established that the developed model provides a slight increase in prediction accuracy while significantly reducing the duration of the training procedure. However, in this case, the main advantage is that all this happens within the bounds of Edge computing, which opens up several benefits of using the developed model in practice.

In Agrifood scenarios, where farmers need to ensure that their produce is safely produced, transported, and stored, they rely on a network of IoT devices to monitor conditions such as temperature and humidity throughout the supply chain. However, managing this large-scale IoT environment poses significant challenges, including transparency, traceability, data tampering, and accountability. Blockchain is portrayed as a technology capable of solving the problems of transparency, traceability, data tampering, and accountability, which are key issues in the AgriFood supply chain. Nonetheless, there are challenges related to managing a large-scale IoT environment using the current security, authentication, and access control solutions. To address these issues, we introduce an architecture in which IoT devices record data and store them in the participant’s cloud after validation by endorsing peers following an attribute-based access control (ABAC) policy. This policy allows IoT device owners to specify the physical quantities, value ranges, time periods, and types of data that each device is permitted to measure and transmit. Authorized users can access this data under the ABAC policy contract. Our solution demonstrates efficiency, with 50% of IoT data write requests completed in less than 0.14 s using solo ordering service and 2.5 s with raft ordering service. Data retrieval shows an average latency between 0.34 and 0.57 s and a throughput ranging from 124.8 to 9.9 Transactions Per Second (TPS) for data sizes between 8 and 512 kilobytes. This architecture not only enhances the management of IoT environments in the AgriFood supply chain but also ensures data privacy and security.