Search Results (8,393)

Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor (CMOS) logic counters (Integrated Circuit (IC) IC 7493 and IC 4017) and inverter-based crystal oscillators (IC 74LS04). The proposed system enables frequency division from 1 MHz down to 1 Hz through a cascade of binary and Johnson counters, enhanced with digitally controlled multiplexers for output signal selection. Unlike conventional timing systems relying on expensive Field-Programmable Gate Array (FPGA) or Global Navigation Satellite System (GNSS)-based synchronization, this approach offers a robust, locally controlled reference clock suitable for IoT nodes without network access. The hardware is integrated with Arduino and ESP32 microcontrollers via General-Purpose Input/Output (GPIO) level interfacing, supporting real-time timestamping, deterministic task execution, and microsecond-level synchronization. The system was validated through Python-based simulations incorporating Gaussian jitter models, as well as real-time experimental measurements using Arduino’s micros() function. Results demonstrated stable pulse generation with timing deviations consistently below ±3 µs across various frequency modes. A comparative analysis confirms the advantages of this CMOS-based timing solution over Real-Time Clock (RTC), Network Time Protocol (NTP), and Global Positioning System (GPS)-based methods in terms of local autonomy, cost, and integration simplicity. This work provides a practical and scalable time reference architecture for educational, industrial, and distributed applications, establishing a new bridge between classical digital circuit design and modern Internet of Things (IoT) timing requirements. Full article

Age of Information (AoI) is a newly introduced metric that quantifies the freshness and timeliness of data, playing a crucial role in applications reliant on time-sensitive information. Minimizing AoI through optimal scheduling is challenging, especially in energy-constrained Internet of Things (IoT) networks. In this work, we begin by analyzing a simplified cognitive radio network (CRN) where a single secondary user (SU) harvests RF energy from the primary user and transmits status update packets when the PU spectrum is available. Time is divided into equal time slots, and the SU performs either energy harvesting, spectrum sensing, or status update transmission in each slot. To optimize the AoI within the CRN, we formulate the sequential decision-making process as a partially observable Markov decision process (POMDP) and employ dynamic programming to determine optimal actions. Then, we extend our investigation to evaluate the long-term average weighted sum of AoIs for a multi-SU CRN. Unlike the single-SU scenario, decisions must be made regarding which SU performs sensing and which SU forwards the status update packs. Given the partially observable nature of the PU spectrum, we propose an enhanced Deep Q-Network (DQN) algorithm. Simulation results demonstrate that the proposed policies significantly outperform the myopic policy. Additionally, we analyze the effect of various parameter settings on system performance. Full article

The global energy transition and digitalization are reshaping traditional production and consumption paradigms. Green hydrogen is emerging as a key element for decarbonizing sectors like industry and transportation, offering a viable alternative to fossil fuels and a pathway toward mitigating climate change. However, implementing green hydrogen supply chains presents challenges related to traceability, operational efficiency, and process certification. This paper explores how blockchain and the Internet of Things can address these challenges and transform the green hydrogen supply chain. Using Morocco as a case study—a country with abundant renewable resources and a strategic focus on green hydrogen—this article proposes innovative technological solutions to support a sustainable energy transition and contribute to a more secure and energy-efficient future. We analyze the current state of research on blockchain, IoT, and green hydrogen, identify key areas for advancement, and present a proposed framework for integrating these technologies. Full article

Technological innovation has significantly transformed the field of statistics, not only in data analysis but also in data collection. Traditional methods based on direct observation have evolved into hybrid approaches that combine passively collected data (e.g., from GPS or accelerometers) with active user input through digital interfaces. This evolution has led to Smart Surveys—next-generation tools that leverage smart devices, such as smartphones and wearables, to collect data actively (via questionnaires or images) and passively (via embedded sensors). Smart Surveys offer strategic value in industrial contexts by enabling real-time data collection on worker behavior, environments, and operational conditions. However, the heterogeneity of such data poses challenges in management, integration, and quality assurance. This study proposes a modular system architecture incorporating gamification elements to enhance user participation, particularly among hard-to-reach worker segments, such as mobile or shift workers. By leveraging motivational strategies and interactive feedback mechanisms, the system seeks to foster greater engagement while addressing critical data security and privacy concerns within industrial Internet of Things (IoT) environments. Full article

The growing demand for scalability and efficiency in Low Power Wide Area Networks (LPWANs) presents significant challenges, particularly due to the increasing number of connected devices and the inherent limitations of the ALOHA protocol, which is widely used in LoRaWAN networks. In this context, this work proposes the design and development of a low-cost dual-channel gateway tailored for Internet of Things (IoT) networks based on LoRa technology. To address the aforementioned challenges, this study explores approaches such as channel activity detection (CAD) and dynamic channel allocation, aiming to reduce collisions and optimize spectrum utilization. Experimental tests were conducted in environments subject to interference from coexisting networks to evaluate the performance of the proposed gateway. The results demonstrated significant improvements in packet delivery rate (PDR) and loss reduction, with PDR exceeding 94% for spreading factors (SFs) ranging from SF7 to SF12. In comparison, the single-channel gateway operating under the same conditions achieved a PDR between 80% and 85%. These results highlight the feasibility of the dual-channel gateway for small- and medium-scale IoT applications in scenarios with multiple coexisting networks. Full article

In the context of Internet of Things (IoT) intelligent systems, the latest research regarding delegation using an access control model has gained attention, reflecting the need for models to support more functionalities in relation to hierarchical delegation. With respect to delegation procedures within access control, issues arise after delegation concerning the permissions to others with respect to revocation. Redundancy and conflict arising from delegation can occur depending on the delegation policies used within the hierarchical structure. This article discusses implementation of positive delegation represented by “YES” and negative delegation represented by “NO”. Furthermore, we also consider permission to delegate positively and negatively represented by (YES and NO). These challenges are addressed by creating additional features in a hierarchical policy model (HPol). The implementation was created using Python (ver. 3.10) code to verify the advantages of the approach, through experimentation under different scenarios. The model also has the capability to manage and adapt features of the Internet of Things (IoT) to a blockchain architecture, enhancing security and verification during the delegation process and increasing the scalability of Internet of Things (IoT) intelligent environment systems. Full article

The proliferation of small, agile unmanned aerial vehicles (UAVs) has exposed the limits of single-sensor surveillance in cluttered airspace. We propose an Internet of Things-enabled integrated sensing and communication (IoT-ISAC) framework that converts cellular base stations into cooperative, edge-intelligent sensing nodes. Within a four-layer design—terminal, edge, IoT platform, and cloud—stations exchange raw echoes and low-level features in real time, while adaptive beam registration and cross-correlation timing mitigate spatial and temporal misalignments. A hybrid processing pipeline first produces coarse data-level estimates and then applies symbol-level refinements, sustaining rapid response without sacrificing precision. Simulation evaluations using multi-band ISAC waveforms confirm high detection reliability, sub-frame latency, and energy-aware operation in dense urban clutter, adverse weather, and multi-target scenarios. Preliminary hardware tests validate the feasibility of the proposed signal processing approach. Simulation analysis demonstrates detection accuracy of 85–90% under optimal conditions with processing latency of 15–25 ms and potential energy efficiency improvement of 10–20% through cooperative operation, pending real-world validation. By extending coverage, suppressing blind zones, and supporting dynamic surveillance of fast-moving UAVs, the proposed system provides a scalable path toward smart city air safety networks, cooperative autonomous navigation aids, and other remote-sensing applications that require agile, coordinated situational awareness. Full article

The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform an evaluation of the 5G infrastructure for sensor networks in order to quantify the performance in terms of energy efficiency and bandwidth within a testing environment. We used an ESP32 sensor with BLE-connected sensing devices for environmental conditions, and a Raspberry Pi with the Waveshare SIM8200EA-M2 5G module for cellular connectivity. We measured the power usage of each component of the system, in real conditions, as well as the power consumption for different bandwidth usage scenarios, and the end-to-end delay of the system. The results showed that the system is capable of achieving the required delay and bandwidth; however, the energy efficiency of the specific setup leaves room for improvement. Full article

In recent years, electrospinning technology has sparked a revolution in the nanoengineering of gas-sensing materials. Nanofibers based on metal oxide semiconductors, carbon materials, or conductive polymers prepared by the electrospinning process have exhibited inspiring properties, including a large specific surface area, porous structure, and nice stability, with bright application prospects in advanced gas sensors. Meanwhile, the increasingly expanding applications of gas sensors, such as the Internet of Things (IoT), the food industry, disease diagnosis, etc., have raised higher sensor performance requirements. To further enhance the gas-sensing performance of nanofibers, the scheme of functionalized nanofiber strategies, either in electrospinning or post-treatment, has been proposed and verified. This review systematically summarized the nanostructures, gas-sensing properties, and functional mechanisms of modified nanofibers. Additionally, the perspectives and challenges regarding electrospun nanofibers for gas sensing were discussed. Full article

With the deep integration of edge computing and Internet of Things (IoT) technologies, the computational capabilities of intelligent edge cameras continue to advance, providing new opportunities for the local deployment of video understanding algorithms. However, existing video captioning models suffer from high computational complexity and large parameter counts, making them challenging to meet the real-time processing requirements of resource-constrained IoT edge devices. In this work, we propose EdgeVidCap, a lightweight video captioning model specifically designed for IoT edge cameras. Specifically, we design a hybrid module termed Synergetic Attention State Mamba (SASM) that incorporates channel attention mechanisms to enhance feature selection capabilities and leverages State Space Models (SSMs) to efficiently capture long-range spatial dependencies, achieving efficient spatiotemporal modeling of multimodal video features. In the caption generation stage, we propose an adaptive attention-guided LSTM decoder that can dynamically adjust feature weights according to video content and auto-regressively generate semantically rich and accurate textual descriptions. Comprehensive evaluations of EdgeVidCap on mainstream datasets, including MSR-VTT and MSVD are analyzed. Experimental results demonstrate that our system demonstrated enhanced precision relative to existing investigations, and our streamlined frame filtering mechanism yielded greater processing efficiency while creating more dependable descriptions following frame selection. Full article

With the rapid advancement of the Internet of Things (IoT), artificial intelligence (AI), and blockchain technologies, educational research has increasingly explored smart and personalized learning systems. However, current approaches often suffer from fragmented integration of health monitoring and instructional adaptation, insufficient prediction accuracy of physiological states, and unresolved concerns regarding data privacy and security. To address these challenges, this study introduces SHARP, a novel blockchain-enhanced wireless sensor networks (WSNs) framework designed for real-time student health monitoring and personalized learning in smart educational environments. Wearable sensors enable continuous collection of physiological data, including heart rate variability, body temperature, and stress indicators. A deep neural network (DNN) processes these inputs to detect students’ physical and affective states, while a reinforcement learning (RL) algorithm dynamically generates individualised educational recommendations. A Proof-of-Authority (PoA) blockchain ensures secure, immutable, and transparent data management. Preliminary evaluations in simulated smart classrooms demonstrate significant improvements: the DNN achieves a 94.2% F1-score in state recognition, the RL module reduces critical event response latency, and energy efficiency improves by 23.5% compared to conventional baselines. Notably, intervention groups exhibit a 156% improvement in quiz scores over control groups. Compared to existing solutions, SHARP uniquely integrates multi-sensor physiological monitoring, real-time AI-based personalization, and blockchain-secured data governance in a unified framework. This results in superior accuracy, higher energy efficiency, and enhanced data integrity compared to prior IoT-based educational platforms. By combining intelligent sensing, adaptive analytics, and secure storage, SHARP offers a scalable and privacy-preserving solution for next-generation smart education. Full article

This paper investigates the error rate performance of simultaneous wireless information and power transfer (SWIPT) systems employing opportunistic amplify-and-forward (OAF) relaying under Rayleigh fading conditions. To support both data forwarding and energy harvesting at relays, a power splitting (PS) mechanism is applied. We derive exact and asymptotic symbol error rate (SER) expressions using moment-generating function (MGF) methods, providing analytical insights into how the power splitting ratio $\rho$ and the quality of source–relay (SR) and relay–destination (RD) links jointly affect system behavior. Additionally, we propose a novel approximation that interprets the SWIPT-OAF configuration as an equivalent non-SWIPT OAF model. This enables tractable performance analysis while preserving key diversity characteristics. The framework is extended to include scenarios with partial channel state information (CSI) and $N_{\mathrm{th}}$ best relay selection, addressing practical concerns such as limited relay availability and imperfect decision-making. Extensive simulations validate the theoretical analysis and demonstrate the robustness of the proposed approach under a wide range of signal-to-noise ratio (SNR) and channel conditions. These findings contribute to a flexible and scalable design strategy for SWIPT-OAF relay systems, making them suitable for deployment in emerging wireless sensor and internet of things (IoT) networks. Full article

The activities of birds present increasing challenges in agriculture, aviation, and environmental conservation. This has led to economic losses, safety risks, and ecological imbalances. Attempts have been made to address the problem, with traditional deterrent methods proving to be labour-intensive, environmentally unfriendly, and ineffective over time. Advances in artificial intelligence (AI) and the Internet of Things (IoT) present opportunities for enabling automated real-time bird detection and repellence. This study reviews recent developments (2020–2025) in AI-driven bird detection and repellence systems, emphasising the integration of image, audio, and multi-sensor data in IoT and edge-based environments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used, with 267 studies initially identified and screened from key scientific databases. A total of 154 studies met the inclusion criteria and were analysed. The findings show the increasing use of convolutional neural networks (CNNs), YOLO variants, and MobileNet in visual detection, and the growing use of lightweight audio-based models such as BirdNET, MFCC-based CNNs, and TinyML frameworks for microcontroller deployment. Multi-sensor fusion is proposed to improve detection accuracy in diverse environments. Repellence strategies include sound-based deterrents, visual deterrents, predator-mimicking visuals, and adaptive AI-integrated systems. Deployment success depends on edge compatibility, power efficiency, and dataset quality. The limitations of current studies include species-specific detection challenges, data scarcity, environmental changes, and energy constraints. Future research should focus on tiny and lightweight AI models, standardised multi-modal datasets, and intelligent, behaviour-aware deterrence mechanisms suitable for precision agriculture and ecological monitoring. Full article

Vehicular ad hoc networks (VANETs) are a critical aspect of intelligent transportation systems, improving safety and comfort for drivers. These networks enhance the driving experience by offering timely information vital for safety and comfort. Yet, VANETs come with their own set of challenges concerning security, privacy, and design reliability. Traditionally, vehicle authentication occurs every time a vehicle enters the domain of the roadside unit (RSU). In our study, we suggest that authentication should take place only when a vehicle has not covered a set distance, increasing system efficiency. The rise of the Internet of Things (IoT) has seen an upsurge in the use of IoT devices across various fields, including smart cities, healthcare, and vehicular IoT. These devices, while gathering environmental data and networking, often face reliability issues without a trusted intermediary. Our study delves deep into implementing Teegraph in VANETs to enhance authentication. Given the integral role of VANETs in Intelligent Transportation Systems and their inherent challenges, we turn to Hashgraph—an alternative to blockchain. Hashgraph offers a decentralized, secure, and trustworthy database. We introduce an efficient authentication system, which triggers only when a vehicle has not traversed a set distance, optimizing system efficiency. Moreover, we shed light on the indispensable role Hashgraph can occupy in the rapidly expanding IoT landscape. Lastly, we present Teegraph, a novel Hashgraph-based technology, as a superior alternative to blockchain, ensuring a streamlined, scalable authentication solution. Our approach leverages the logical key hierarchy (LKH) and packet update keys to ensure data privacy and integrity in vehicular networks. Full article

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article