This paper presents an Internet of Things (IoT)-based system for real-time student identification and monitoring in school transportation environments. The proposed solution integrates an ESP32-CAM edge device with an artificial intelligence facial recognition model to automatically identify students when boarding or leaving the school bus. The system architecture includes sensing, gateway, server, and application layers, enabling real-time communication between the bus device, cloud services, and a mobile application used by parents and school administrators. For the facial recognition component, facial encodings were extracted from images and processed using preprocessing techniques such as face alignment, normalization, and data augmentation. Experiments were conducted using a dataset derived from the Labeled Faces in the Wild (LFW) dataset. Several models were evaluated, and an optimized Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) achieved the best performance with an accuracy of 99.60 %. The prototype demonstrates real-time recognition with processing latency below 200 ms, enabling automated attendance and instant notifications. The proposed architecture offers a scalable and cost-effective solution for improving safety and monitoring in school transportation systems.

The demand for smart safety solutions has increased, especially for vulnerable groups such as women, children, and the elderly. This study presents a wearable-based intelligent safety and emergency alert system that enables real-time monitoring and rapid response in critical situations. The system combines wearable sensors, a mobile application, and cloud communication to detect distress using signals such as panic button activation, abnormal motion, and irregular vital signs. When a potential emergency is detected, instant alerts are sent to pre-defined contacts and emergency services along with the user’s real-time location. The proposed solution integrates embedded systems, wireless communication, and internet of things technologies to ensure reliability and ease of use. The wearable device is lightweight and discreet, while the mobile application supports both manual and automated alerts. Ethical aspects are also addressed, with a focus on data privacy, secure communication, and user consent. This system aims to improve personal safety and reduce emergency response time.

Health 5.0 increasingly involves medical robots and haptic systems that sustain physical interaction with patients and clinicians. This proof-of-concept study examined fatigue-related surface electromyography (sEMG) spectral changes during a 400 s repetitive haptic-writing task in 20 adults. Five upper-limb muscles were monitored at 1000 Hz, and mean frequency (MNF) and median frequency (MDF) trajectories were summarized by fitted start-to-end spectral decline and combined into arm-level indices. MDF had the higher association with the archived participant-level fatigue-analysis score (r=0.954 versus r=0.783; Δr=0.171; Holm-adjusted p=0.0035). Small-sample influence analysis supported the stability of this within-sample ordering: after omitting each participant in turn, Δr remained positive in 20/20 analyses (range 0.125–0.218), although the exact paired label-swap sensitivity test remained inconclusive (p=0.082). Exploratory leave-one-participant-out calibration produced lower held-out error for MDF (MAE 4.38, RMSE 5.87 percentage points) than for MNF (MAE 9.24, RMSE 12.02 percentage points). The raw seven-category Q2 responses and the archived 0–100 analysis score are reported as separate data products because they are not numerically equivalent under direct linear rescaling. Accordingly, the results identify MDF as the more promising spectral summary for prospective validation in this task, rather than establishing universal superiority or numerical replacement of subjective fatigue. The experiment was a laboratory haptic-writing study and did not test a surgical robot, rehabilitation robot, patient population, or clinical controller; Health 5.0 is therefore presented as a translational motivation rather than a demonstrated application.

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative.

The rapid urbanization of modern cities necessitates intelligent solutions for efficient resource management, sustainability, and improved quality of life. Smart cities leverage technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to optimize urban infrastructure and services. A key innovation in this transformation is the integration of digital twins dynamic virtual models that replicate physical urban environments and individual behaviors in real time. This paper explores the role of digital twins in smart cities, highlighting their applications in urban planning, transportation, healthcare, and environmental monitoring. The study also examines the interaction between digital twins of cities and individuals, enabling hyper-personalized services and data-driven governance. While digital twins offer significant benefits, their implementation raises challenges related to data privacy, cybersecurity, and ethical considerations. Addressing these issues is critical for fostering sustainable and inclusive urban ecosystems. This paper concludes with future research directions, emphasizing the need for robust AI-driven recommendation systems, secure data frameworks, and policy measures to enhance the effectiveness of digital twins in smart cities.

The metaverse—a persistent, multiuser fusion of digitally augmented reality and computer-generated virtuality— is emerging as a programmable substrate for identity, assets, and interaction. Its heterogeneous stack (XR clients, engines/SDKs, Web3 rails, wallets, marketplaces) enlarges the attack surface. This paper contributes: (i) a structured threat taxonomy specialized for Web3/XR platforms; (ii) explicit system and adversary models; (iii) a risk quantification scheme combining behavioral and on-chain signals; and (iv) a data-driven defense architecture aligning decentralized identity, wallet/custody guardrails, analytics, AI-aided detection, and policy instrumentation. We further instantiate these controls in the Medical MeTAI context, where confidentiality, integrity, and provenance requirements are stringent.

In today’s digital landscape, the Internet of Things (IoT) is playing an increasingly vital role in healthcare by enabling smart, connected applications that enhance patient monitoring, diagnostics, and overall well-being. However, the deployment of these IoT-based healthcare solutions presents several challenges, particularly regarding the efficient use of system resources such as data acquisition, storage, processing, and network bandwidth. Healthcare environments, where continuous and reliable data transmission is critical, generate vast amounts of medical and environmental data that must be transmitted through various communication technologies (Wi-Fi, Bluetooth, LTE, etc.). To address the issue of network congestion and resource constraints, we propose an intelligent data compression strategy tailored to healthcare-focused IoT systems. This approach optimizes data transmission by reducing the volume of data during the acquisition stage, while a prioritization mechanism ensures that the most critical health-related information is transmitted in real time. To validate our approach, we implemented it in an air quality monitoring system, focusing on pollutants with significant impacts on human health. The results demonstrate that our method effectively reduces network load while preserving the quality and relevance of transmitted healthcare data.

Advancements in technology, particularly Virtual Reality (VR), Augmented Reality (AR), and eXtended Reality (XR), have brought unprecedented immersive experiences. This paper examines two of the most promising XR technologies, the Metaverse and Digital Twin (DT), focusing on their inherent privacy challenges. The discussion extends to envisaged and theoretical proof-of-concept solutions, including entropy-based models to quantify privacy risks, differential privacy mechanisms for securing personal data, and blockchain implementations to ensure data integrity. This paper also explores the integration of decentralized identity systems, secure biometric authentication, and emerging regulatory frameworks. We conclude by proposing future research directions, emphasizing the role of blockchain, advanced AI, and legal standards in safeguarding the privacy of immersive digital environments.

The emergence of the Metaverse as a 3D virtual world has introduced new challenges in networking, communications, and security. As the Metaverse continues to evolve, protecting users’ digital content and personal data has become a critical concern. Blockchain, with its decentralized, immutable, and transparent features, offers a promising solution to address these challenges. This paper provides a comprehensive review of blockchain applications in the Metaverse. We examine blockchain’s role in data acquisition, storage, sharing, interoperability, and privacy preservation, alongside its impact on enabling technologies such as IoT, digital twins, AI, and big data. Finally, we identify future research directions to foster innovation in integrating blockchain with the Metaverse.

The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.

This paper presents an innovative method leveraging artificial intelligence (AI) to enhance energy efficiency and proactive management of electrical grids. Our dynamic energy distribution strategy (DEDS), powered by AI and reinforcement learning (RL), utilizes iterative environmental simulations in Matlab. Sensor data, including energy demand and renew-able energy (RE) production, informs the AI algorithm, which dynamically adjusts energy distribution strategies. Real-time (RL) implementation of control actions ensures adaptability to changing electrical system conditions. Visual analysis of two AI-adjusted strategies, with and without sensor input, highlights the effectiveness of our approach, demonstrating a mere 9% error rate. This research showcases AI's pivotal role in optimizing energy distribution amid evolving system dynamics.

A network of 3D virtual worlds centered on social interaction is known as a metaverse. It is frequently characterized as a hypothetical version of the Internet as a single, worldwide virtual environment in science fiction and futuristic literature, made possible by the usage of virtual reality (VR) and augmented reality (AR) headgear. The basic blocks of the metaverse's architecture are mostly already in advanced stages of development. On the other hand, many others are still several years away from being useful. We begin by examining its structures before moving on to the ingenious protection and security challenges brought up by this new viewpoint. The goal is to layout the ground for an insightful discussion about user privacy, integrity, and authentication in the metaverse, as well as countermeasures and security perspectives with humans in and out of the loop. Finally, we highlight some of the broad yet recurring effects on a variety of settings in terms of data explosion and exploitation, distributed denial of service, device vulnerability, and polarization and radicalization in a singleton world.

As technology has advanced, people's lives have been transformed by the virtual world, which has been created by technologies such as the Internet, computers, artificial intelligence, and hardware. The metaverse, a new social ecology connecting the physical and virtual worlds, is rapidly expanding as the demand for virtual reality grows. Privacy, security, high synchronization, and low latency have all been challenged as data volumes and value have grown and the metaverse continues to evolve. As blockchain and intelligent networking technologies continue to evolve, these challenges can be addressed and the metaverse's needs for trusted construction, continuous data interaction, and computing can be satisfied. In order to provide immersive experiences in the metaverse, a comprehensive review of blockchain's role and benefits is essential. The purpose of this survey is to discuss the metaverse's development trend, architecture, and characteristics. This review paper is novel in providing an overview of existing blockchain research, including overviews, applications, and challenges. Furthermore, we summarized the metaverse's applications, emphasizing their significance and areas of development. Using the survey, we are able to discuss open issues, challenges, and future research directions.

This paper introduces a parallel architecture designed for real-time image processing applications, utilizing a combination of digital signal processor (DSP) and field-programmable gate array (FPGA) components for optimal performance. The FPGA component features a first in, first out (FIFO) interconnection network and a specialized data communication protocol that enables effective interconnectivity between three DSPs (TMS320C6414). The performance metrics of the experimental prototype were impressive, as it exhibited both data, by leveraging its efficient image processing capabilities, the system achieves a harmonious balance in parallel processing.

The sudden outbreak of the novel coronavirus (nCoV-19, COVID-19) and its rampant spread led to a significant number of people being infected worldwide and disrupted several businesses. With most of the countries imposing serious lockdowns due to the increasing number of fatalities, the social lives of millions of people were affected. Although the lockdown led to an increase in network activities, online shopping, and social network usage, it also raised questions On the mental wellness of society. Interestingly, excessive usage of social networks also witnessed humor traveling across the Internet in the form of Internet Memes during the lockdown period. Humor is known to affect our well-being, decision-making, and psychological systems. In this paper, we have analyzed the Internet Meme activity in Social Networks during the COVID-19 Lockdown period. As humor is known to relieve individuals from psychological stress, it is necessary to understand how human beings adopted Internet Memes for coping up with the lockdown stress and stress-relieving mechanism during the lockdown period. In this paper, we have considered thirty popular memes and the increase in the number of their captions within the period (September 2017 to August 2020). An increase in Internet Meme activity since the lockdown period (March 2020) depicts an increase in online social behavior. We analyze the internet meme activity in social networks during the COVID-19 lockdown period using random forest, multi-layer perceptron, and instance-based learning algorithms followed by data visualization using line graph and Heat Map (8 & 15 clustered). We also compared the performance of the models using evaluation parameters like mean absolute error, root-mean-squared error & Kappa statistics and observed that random forest and instance-based learning algorithms perform better than multi-layer perceptrons. The result indicates that random forest and instance-based learning classifiers are having near perfect classification tendencies whereas multi-layer perceptrons showed around 97% classification accuracy.

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

The advancement of modern multimedia and data-intensive classes of applications demands the development of hardware that delivers better performance. Due to the evolution of 5G, Edge-Computing, the Internet of Things, Software-Defined networks, etc., the data produced by the devices such as sensors are increasing. A software-Defined network is a powerful paradigm that is capable of automating networking and cloud computing. Software-Defined Network has controllers, devices, and applications which produce a huge amount of data. The processing of data inside the device as well as between the devices needs a better hardware architecture with more cores to ensure speedy performance. The System-on-Chip approach alone will not be capable to handle this dense core comprised of hardware. We have to blend Network-on-Chip along with System-on-Chip to increase the potential to include more cores capable to handle more threads. Artificial Intelligence, a key enabler in next-generation devices is capable of producing a better architecture design with optimized performance. In this paper, we are discussing and endeavouring how System-on-Chip, Network-on-Chip, Software-Defined Networks, and Artificial Intelligence can be physically, logically, and contextually incorporated to deliver improved computation and networking outcomes.

Blockchain technology has lately surged to the peak of scientific and industrial priorities due to its possible benefits over a broad array of industries. This is owing to their demonstrated abilities to address many of the difficulties that are now inhibiting advancements in a variety of industries. Securely gathering and transmitting transaction information, allowing effective and systematic logistics operations, as well as boosting clarity in the entire system are just a few of those concerns. The significant economic and operational benefits of blockchain can benefit a wide range of applications in numerous industries. People, enterprises, organizations, and properties can all benefit from digital identification, which can lead to a flood of new business prospects. Applications in finance, energy, transportation, healthcare, and industrial supply chain are growing rapidly and proving to be tremendously advantageous. In order to build more functional and productive industrial applications, several outstanding concerns must be studied and analyzed further. This article analyses and investigates the possibilities, advantages, as well as drawbacks of using blockchain in a variety of commercial operations. In addition, the research aims to define the prerequisites for implementing blockchain for various industrial applications. The study uncovers several opportunities for blockchain adoption in several industries; yet, there are still some hurdles to overcome to enhance the benefits of this technology and ensure better utilization.

Undoubtedly, the world has so far faced a pandemic which is reshaping daily lives and business activities. Even at current endemic stages, special focus on maintaining physical distancing norms for curbing the expeditious spread of the disease, many institutions, individuals and industries rely on communications networks or telecoms for ensuring service consistency to avoid complete termination of their business operations and other activities. This has put enormous pressure on mobile networks and communication systems thereby making the technology experts to think more about introducing rapid speed, vast coverage and high connectivity networks. The extensive application of fresh communication networks and enabling technologies have impelled the advent of 6G communication networks. As 6G is still in its inception phase, its complete realization requires a proper and high understanding of diverse quantitative and qualitative factors supporting its deployment. From this standpoint, this survey article intends to deeply explore 6G networks, their significance and prerequisites. This paper provides a succinct theoretical background of 6G technology, and reviews the diverse enabling technologies and existing works undertaken on core technologies. It explores the prevailing gaps in research for providing readers to gain information regarding challenges in perfect 6G network realization and implementation thus paving the road for a successful 6G vision.

Implementing Internet of Things (IoT)-based services in commercial aviation gives a painless and effortless experience to passengers and crew members, but the mitigation of cyber-attacks is challenging. Available solutions are not enough to overcome various cyber security attacks. Hence, we need more attention to overcome the challenges and cyber risks in commercial aviation. The aim of this paper is to highlight the various factors of cyber threat in avionics and discuss the major security studies conducted to date. The goal is to layout the ground for an insightful discussion about Artificial Intelligence (AI) potential, challenges, and solutions for cybersecurity in avionics. We will focus on how AI and IoT can be used to identify cyber security threats, type of attacks, and infrastructure vulnerabilities. This information will help us in developing effective preventive measures in the future.