Search Results (452)

This work introduces a wine glass-shaped planar ultra-wideband (UWB) antenna. The antenna achieves a compact form factor by reducing lateral width through Bézier curve shaping and a trident feed, while maintaining length for low-frequency operation. The wine-glass-shaped radiator increases shunt capacitance and enhances midband impedance matching, as demonstrated by equivalent circuit analysis, while the trident feed improves matching at higher frequencies. This design yields a 92% fractional bandwidth (3.2–8.7 GHz) within a compact volume of $0.37{\lambda }_0\times 0.13{\lambda }_0\times 0.0013{\lambda }_0$. The prototype is fabricated on two 50-μm-thick polyimide flexible copper-clad laminates (FCCL), and its performance is evaluated in an anechoic chamber. The measured results demonstrate omnidirectional radiation with an efficiency of over 80% across the UWB band. With broad operational range and compactness, the antenna is well-suited for IoT and wearable sensing applications. Full article

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

To address the challenges linked to climate change, rapidly increasing urbanization, and food security necessity, this study explores the potential of smart, low-cost innovative cultivation systems for modules on facades as nature-based solutions (NBSs) to improve building energy efficiency, urban food production, and sustainability. Innovative cultivation systems were studied and implemented in the horizontal experimental setup, with a focus on sub-irrigation techniques with terracotta pots, ozonated water, and IoT use. The best eco-smart irrigation system was selected considering both plant growth and the water savings obtained (up to 57.14%) in comparison to the traditional method. With the implementation of this system, a vertical green module (VGM) was designed, allowing for efficient distribution and water savings. The positive effects in terms of temperature reduction and energy behavior were validated by comparing two office rooms: one without VGM and the other with VGM in a Mediterranean city. The drop in internal temperatures achieved was up to 3–4 °C during the hot days of the experimental campaign. The uptake of this low-cost and smart prototype can be useful to support the enhancement of energy-efficient, eco-sustainable, and self-sufficient buildings and urban spaces, contributing to creating more climate-resilient cities and promoting sustainable urban agriculture. Full article

The increasing demand for sustainable and autonomous monitoring solutions in critical infrastructure has driven interest in Green Internet of Things (G-IoT) systems. This paper presents an analytical and experimental framework for designing energy-efficient, self-sustaining pipeline monitoring systems that leverage renewable energy harvesting and low-power operation techniques. We propose a hybrid approach combining solar energy harvesting with energy-saving strategies such as adaptive sensing, duty cycling, and distributed computing to extend the lifetime of IoT nodes without human intervention. Using real-world irradiance data and energy profiling from a prototype testbed, we analyze the impact of solar panel sizing, energy storage capacity, energy-saving strategies, and energy leakage on the energy balance of IoT nodes. The simulation results show that, with optimal dimensioning, harvested solar energy can sustain pipeline monitoring operations over multi-year periods, even under variable environmental conditions. We investigated the influence of design parameters such as duty cycling, solar panel area, the capacity of the energy storage system, and the energy leakage coefficient on energy performance metrics such as the autonomy or lifetime of the node (time required to drain all the stored energy), which is an important design object. This framework provides practical design insights for the scalable deployment of G-IoT systems in energy-constrained outdoor environments. Full article

Traditional construction safety management, reliant on manual inspections and heuristic judgments, increasingly fails to address the dynamic, multi-dimensional risks of modern projects, perpetuating fragmented safety governance and reactive hazard mitigation. This study proposes an integrated building information modeling (BIM)–AI platform to unify safety supervision across the project lifecycle, synthesizing spatial-temporal data from BIM with AI-driven probabilistic models and IoT-enabled real-time monitoring for sustainable construction safety management. Employing a Design Science Research methodology, the platform’s phase-agnostic architecture bridges technical–organizational divides, while the Multilayer Neural Risk Coupling Assessment framework quantifies interdependencies among structural, environmental, and human risk factors. Prototype testing in real-world projects demonstrates improved risk detection accuracy, reduced reliance on manual processes, and enhanced cross-departmental collaboration. The system transitions safety regimes from compliance-based protocols to proactive, data-empowered governance. This approach offers scalability across diverse projects. The BIM-AI intelligent fusion platform proposed in this study builds an intelligent construction paradigm with synergistic development of safety governance and sustainability through whole lifecycle risk coupling analysis and real-time dynamic monitoring, which realizes a proactive safety supervision system while significantly reducing construction waste and accident prevention mechanisms. Full article

Virtual reality (VR)/the metaverse is transforming into a ubiquitous technology by leveraging smart devices to provide highly immersive experiences at an affordable price. Cryptographically securing such augmented reality schemes is of paramount importance. Securely transferring the same secret key, i.e., obfuscated, between several parties is the main issue with symmetric cryptography, the workhorse of modern cryptography, because of its ease of use and quick speed. Typically, asymmetric cryptography establishes a shared secret between parties, after which the switch to symmetric encryption can be made. However, several SoTA (State-of-The-Art) security research schemes lack flexibility and scalability for industrial Internet-of-Things (IoT)-sized applications. In this paper, we present the full architecture of the PRIVocular framework. PRIVocular (i.e., PRIV(acy)-ocular) is a VR-ready hardware–software integrated system that is capable of visually transmitting user data over three versatile modes of encapsulation, encrypted—without loss of generality—using an asymmetric-key cryptosystem. These operation modes can be optical character-based or QR-tag-based. Encryption and decryption primarily depend on each mode’s success ratio of correct encoding and decoding. We investigate the most efficient means of ocular (encrypted) data transfer by considering several designs and contributing to each framework component. Our pre-prototyped framework can provide such privacy preservation (namely virtual proof of privacy (VPP)) and visually secure data transfer promptly (<1000 ms), as well as the physical distance of the smart glasses (∼50 cm). Full article

Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEaaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security. Full article

Flood incidents occur annually in Taman Negara Endau Rompin Selai Bekok due to continuous substantial rains during the rainy period. The absence of a structured flood tracking and detection system limits effective information dissemination regarding flooding to the public; currently, visitors are only informed at office counters. This inefficient conventional method should be upgraded to a real time flood monitoring and alert system utilizing Internet of things (IoT) technology. UTHM personnel have requested the development of an easily accessible flood detection system via tablets or smartphones to efficiently relay flood information to visitors. Consequently, a flood detection system called “Floodwatch” was developed. The prototype provides live water level readings, air temperature, humidity, and flood images to users. The prototype has undergone rigorous testing to ensure stability, consistency, and accuracy, enabling its effective utilization. The Floodwatch system aims to enhance safety and awareness during flood events in the Taman Negara Endau Rompin Selai Bekok area. Full article

The convergence of Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Things (IoT) offers transformative potential across numerous sectors. However, existing studies often examine these technologies independently or in limited pairings, which overlooks the synergistic possibilities of their combined usage. This systematic review adheres to the PRISMA guidelines in order to critically analyze peer-reviewed literature from highly recognized academic databases related to the intersection of VR, AI, and IoT, and identify application domains, methodologies, tools, and key challenges. By focusing on real-life implementations and working prototypes, this review highlights state-of-the-art advancements and uncovers gaps that hinder practical adoption, such as data collection issues, interoperability barriers, and user experience challenges. The findings reveal that digital twins (DTs), AIoT systems, and immersive XR environments are promising as emerging technologies (ET), but require further development to achieve scalability and real-world impact, while in certain fields a limited amount of research is conducted until now. This review bridges theory and practice, providing a targeted foundation for future interdisciplinary research aimed at advancing practical, scalable solutions across domains such as healthcare, smart cities, industry, education, cultural heritage, and beyond. The study found that the integration of VR, AI, and IoT holds significant potential across various domains, with DTs, IoT systems, and immersive XR environments showing promising applications, but challenges such as data interoperability, user experience limitations, and scalability barriers hinder widespread adoption. Full article

The integration of 3D printing with smart infrastructure presents a transformative opportunity in urban planning, construction, and engineering, enhancing efficiency, flexibility, and sustainability. By leveraging additive manufacturing alongside digitalization, artificial intelligence (AI), and the Internet of Things (IoT), this technology enables the creation of customized, lightweight, and sensor-embedded structures. This work analyzes both the advantages and challenges of applying 3D printing in smart infrastructure, focusing on material optimization, rapid prototyping, and automated fabrication, which significantly reduce construction time, labor costs, and material waste. Applications such as 3D-printed bridges, modular housing, and IoT-integrated urban furniture exhibit its potential in contributing towards resilient and resource-efficient cities. However, despite these benefits, significant challenges hinder large-scale adoption. Issues of scalability, particularly in the fabrication of large and load-bearing structures, remain unresolved, requiring advancements in high-speed printing techniques, material reinforcement strategies, and hybrid construction methods. Furthermore, regulatory uncertainties and the absence of standardized guidelines create barriers to implementation. The lack of comprehensive building codes, certification protocols, and quality assurance measures for 3D-printed structures limits their widespread acceptance in mainstream construction. Overcoming these limitations necessitates research into AI-driven process optimization, multi-material printing, and international standardization efforts. By assisting towards overcoming these challenges, 3D printing has the potential to redefine urban development, making infrastructure more adaptive, cost-effective, and environmentally sustainable. This work provides a critical evaluation of the current capabilities and limitations of 3D printing in smart infrastructure towards achieving full-scale implementation and regulatory compliance. Full article

The increasing demand for emerging applications like IoT or drones has boosted the interest of industry and academia in DC-DC converters. Due to their high performance, non-isolated buck DC-DC converters have become one of the most common configurations for covering the power demand of portable devices. The current trend focuses on manufacturing these integrated circuits (IC) using surface-mount technology packaging. However, this technology presents disadvantages compared to through-hole devices in pursuing a quick functional circuit. This work aims to guide designers in choosing the most suitable integrated THT buck converter to develop a fast prototype. A comparative market analysis was conducted considering five integrated chip manufacturers to identify the most adequate ICs for this purpose. Then, a comparative experimental study focused on the buck converter LM2576-ADJ by Texas Instruments was carried out. The analysis aims to determine the performance of this IC mounted in a breadboard and stripboard compared to a demonstration board based on SMT technology provided by the manufacturer. Despite their shortcomings, these quick implementations performed remarkably well regarding, among others, line regulation and load regulation (0.37% and –0.33%, respectively), as well as efficiency (up to 79.9%), which indicates that their electrical response was not compromised. Full article

This paper investigates the feasibility of employing expert knowledge-based Explainable Artificial Intelligence (XAI) for smart house control through low-power Internet of Things (IoT) devices that possess limited computational capabilities. By integrating Explainable AI, we seek to enhance the transparency of the model’s decision-making process, thereby increasing its reliability for the end user. The Arduino Uno board was selected for IoT development because of its extensive popularity and affordability. A model of heating control has been developed using temperature sensors based on the presence of residents in the room. The operational prototype was evaluated by measuring the time taken between data input and decision-making, accompanied by an explanation, to identify any potential bottlenecks that may hinder the performance of the microcontroller. To enhance communication security, we developed a pseudo-random number generation function using chaos-based cryptography with hardware implementation, thus improving communication security without incurring additional computational costs. The method has demonstrated a time efficiency improvement of up to 67% for novice users, 58% for intermediate users, and 50% for expert users. Full article

Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype. Full article

Stress is a multifaceted physiological and psychological response that impacts health in diverse ways. This work introduces an IoT- and ML-based wearable stress detection prototype system for elderly care. The prototype developed utilizes Heart Rate, Skin Temperature, and GSR (Galvanic Skin Response) sensors, integrated with data input for real-time analysis. Among different prediction models, Random Forest was found to achieve the highest performance measured in terms of Accuracy (95.06%), Precision (95.22%), Recall (95.06%) and F1-Score (94.38%) and hence was employed for the stress-prediction purpose. Validated on old age home residents (SHEOWS, New Delhi), the device demonstrated satisfactory performance, enabling personalized care and improved stress management through precise, data-driven insights. This is preliminary research which needs to be extended appropriately in the future for further improvements and will work as an input for stress-reduction techniques for elderly people. Full article

This paper presents the development of an energization system prototype for IoT sensor nodes using Peltier cells as energy harvesters; its operation is optimized by applying a maximum power point tracking algorithm (MPPT) to capture as much electrical energy as possible, even if the cell temperature conditions have variations. In the IoT sensor node, a power management algorithm that works in accordance with the measurement and transmission operations can extend the node operating time, to obtain a greater amount of information and reducing the need for battery maintenance. The proposed methodology consists of developing an energization system, as well as the IoT sensor node. The energization system consists of a block of Peltier cells to obtain up to 4 V, a SEPIC-type DC-DC converter, and a 3.7 V lithium battery for energy storage. The converter works in a closed loop with the MPPT algorithm and delivers a voltage that guarantees the maximum power transfer to the battery. The sensor node was developed based on the ESP8266 development board, it allows data acquisition of temperature, humidity, light intensity, presence, and sound. The node transmits this information to the Ubidots platform for real-time visualization; to take advantage of its processing capacity, MPPT and energy management algorithms are also implemented. The results showed that to obtain a minimum voltage of 3.3 V in the energization system, a temperature difference of $59\pm 1$ °C between the plates of the Peltier cells is required. The MPPT algorithm allows working at the maximum power point and keeps the power delivered to the battery stable, with small transients when the information is transmitted; however, the overshoot and the settling time are reduced and do not affect the node operation. Full article