Search Results (5,434)

The sport of orienteering requires athletes to reach specific points marked on a map (called “punching stations”) in the shortest possible time. Currently, the recording of athletes’ passages through the stations is performed offline. In addition to delays in generating intermediate and final rankings, this approach often leads to detection errors and potential cheating related to the lack of authentication of an athlete’s actual passage at a given station. This paper aims to define and design a system enabling three main functionalities: 1. real-time monitoring of athletes’ trajectories through a sensor network connected to control stations; 2. multi-modal authentication of athletes at each station; and 3. immutable certification of each athlete’s passage through blockchain-based recording. System performance is evaluated in terms of wireless network coverage and data collection efficiency across three representative environments: urban, rural, and forested areas. Results are obtained through a measurement campaign for two dedicated wireless technologies: ZigBee for local mesh network and LoRa for long-range links to connect local mesh networks to the cloud over the Internet, which is then accessed by the race organizers. Furthermore, two supporting subsystems are described, addressing athlete authentication and data integrity assurance, as well as a blockchain recording for the overall event management framework. Results are in terms of coverage distances for both technologies, proving highly effective across varied terrains. Field tests demonstrated significant communication capabilities, achieving distances of up to 1800 m in open spaces. Even in challenging, dense wooded environments, the system maintained reliable coverage, reaching transmission distances of up to 600 m. Local ZigBee links between punching stations achieved ranges between 70 and 150 m in forested areas. These findings validate the use of a wireless multi-hop network designed to minimize packet loss and ensure reliable data delivery in competitive scenarios. The feasibility is also investigated in terms of WSN performance, delay analysis and power consumption evaluation. Full article

With the rapid development of rail transit, how to power low-energy monitoring systems for the vast and complex infrastructure in the rail transit system is becoming an urgent problem. To achieve green and intelligent rail transit infrastructure while ensuring long-term operational safety, harvesting vibration energy from tracks to power wireless sensor networks has become a research hotspot. This paper designs a track vibration energy harvester based on a broadband magnetic levitation structure. First, a dynamic model of the harvester is established, and the corresponding dynamic equations, energy–velocity relationship, and system transfer function are derived. Also, by simulating electromagnetic interactions, the distribution pattern of magnetic density inside the energy harvester is revealed. Next, the response characteristics of the energy harvester are analyzed under single-frequency and multi-frequency excitation conditions. Using the Runge-Kutta algorithm for computational analysis, the optimal structural parameters of the energy harvester are designed. Finally, a magnetic levitation energy harvester prototype is constructed. Experimental validation confirmed the feasibility of the energy harvester and its adaptability to low-frequency vibration environments. Full article

Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy consumption with deep learning–assisted cluster-head (CH) selection. First, an analytical framework is developed to determine the distance-constrained CH eligibility region and the optimal number of clusters, thereby minimizing redundant transmissions and balancing energy consumption. Then, a genetic algorithm (GA) is used to determine the best cluster head configuration. These configurations are then trained by a ResNet-50 deep network and averaged to reduce noise, allowing for real-time cluster head prediction without repeatedly performing expensive heuristic optimization, resulting in more steady performance. Extensive simulations under various network scales demonstrate that HEOCP extends network lifetime by up to 60% compared with conventional LEACH and GA-based approaches, effectively delaying the first-node death and improving overall energy efficiency. Furthermore, the hybrid GA–ResNet framework exhibits high scalability and computational efficiency, making it suitable for large-scale IoT deployments. The results confirm that integrating analytical energy modeling with deep learning provides a powerful and sustainable paradigm for intelligent energy management in future IoT-enabled WSNs. Full article

This study presents a low-cost wireless sensor network (WSN) integrated with an Internet of Things (IoT) platform for continuous monitoring of agro-environmental parameters relevant to rice harvest decision support. Solar-powered sensor nodes equipped with temperature-humidity (DHT22) and light intensity (BH1750) sensors were deployed in a Pathum Thani 1 rice field in Si Prachan, Suphan Buri province, Thailand. Environmental data were recorded hourly from June to September 2025 and transmitted wirelessly to a cloud-based dashboard for real-time visualization. Growing Degree Days (GDD) were calculated from measured air temperature using a literature-based base temperature, and cumulative GDD (CGDD) was used to track rice growth progression across vegetative, reproductive, and grain-filling stages. The system demonstrated stable long-term operation and continuous data acquisition under field conditions. Observed CGDD trends were consistent with reported growth-stage thresholds for the studied rice variety, while measured light intensities ranged from 36,900 to 37,810 lx, relative humidity remained consistently high throughout the season, and air temperatures varied between daily minima of 23.5–25.2 °C and maxima near 35.4 °C, which are suitable for rice photosynthesis and development. The seasonal CGDD increased linearly to 580.3, 1189.9, 1593.7, and 2385.7 °C by the end of June, July, August, and September, respectively, exhibiting a strong linear relationship with days after 1 June 2025 (R² = 0.9999), which confirms stable thermal accumulation throughout the growing season. Full article

The deployment of robust Internet of Things (IoT) networks within smart buildings requires a thorough understanding of radio propagation in complex indoor environments. Long Range (LoRa) technology is a promising solution for such applications due to its long range and low power consumption. However, its performance in multi-floor structures is heavily influenced by site-specific propagation conditions. This paper presents an empirical characterization of LoRa signal propagation at 433 MHz within a four-story university building. Extensive measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) were conducted to model both large-scale and small-scale fading effects. A log-distance path loss model with a Floor Attenuation Factor (FAF) was derived, yielding a path loss exponent of $n=2.53$, an FAF of $5.52$ dB per floor, and a log-normal shadowing standard deviation of $\sigma =6.93$ dB. Time-varying small-scale fading was successfully characterized by a Markov-modulated process (Markov Small-Scale Fading). Furthermore, a non-linear relationship between RSSI and SNR was identified and modeled using a four-parameter logistic function, revealing a dynamic range of approximately 30 dB for the transceivers and a minimum measurable RSSI of −125 dBm. The results validate the proposed models and demonstrate that LoRa can provide reliable, building-wide wireless sensor coverage, offering essential guidelines for the planning and deployment of indoor IoT infrastructure in multi-floor environments. Full article

The proliferation of Internet of Things (IoT) devices and services creates not only significant benefits but also new security threats. Classical information encryption techniques are not suitable for resource-constrained edge modules, thereby generating the demand for lightweight and efficient data protection algorithms. This work presents a novel dynamical parameter estimation scheme for chaotic oscillators, applied to physical-layer authentication (PLA). The proposed approach relies on the receiver’s capability to estimate a selected parameter of the transmitter’s oscillator determined by circuit configuration from the received chaotic signal using a locally synchronized oscillator, thereby enabling secure authentication based on a hardware-encoded identifier. The scheme is intended to complement a chaos-based wireless sensor network (WSN) architecture, where sensor nodes (SNs) implement analog chaotic oscillators, and the gateway operates discrete-time models. The Vilnius chaotic oscillator was chosen to validate the proposed PLA scheme. A rigorous bifurcation analysis of analytical, SPICE and discrete oscillator models was first conducted to identify parameter regions that preserve chaotic dynamics, establishing correspondence between models to guarantee the feasibility of parameter estimation across implementations. The digital realization of the parameter estimator demonstrated accurate and stable operation, with a small and nearly constant estimation relative error not exceeding 1.01%. Key performance metrics were analyzed, including estimation time, precision, and noise robustness. A tradeoff between estimation speed and accuracy was identified, particularly under noisy channel conditions. Finally, the influence of the receiver’s native oscillator parameter on distinguishable transmitter parameter ranges was demonstrated, highlighting the configurability and security potential of the proposed system against unauthorized transmissions. Full article

Wireless sensor networks (WSNs) are foundational to modern smart environments, supporting applications ranging from healthcare and precision agriculture to industrial control and disaster response. Despite their potential, WSNs remain constrained by a limited battery life, packet loss, variable throughput, latency, and security vulnerabilities. This paper extends Horse Herd Leadership Optimization (HHLO), a bio-inspired metaheuristic modeling herd leadership, synchronization, and exploration to drive energy-aware clustering and trust-aware routing. HHLO rotates cluster-head leadership in order to balance load, injects chaotic exploration in order to avoid premature convergence, and incorporates a continuously updated node trust score directly into the routing cost in order to exclude unreliable or malicious nodes. Extensive MATLAB simulations with 1000 nodes deployed over a 1000 m × 1000 m² field for 400 rounds, under both static and mobile settings, demonstrate HHLO’s effectiveness. Compared to baseline approaches, HHLO achieves residual energy improvement of 12–21%, throughput gains of 14–23%, Packet Delivery Ratio (PDR) increase of 6–12%, and network lifetime extension of 18–32%; it also achieves an energy balance factor (EBF) of 0.91 and a trust balance factor (TBF) of 0.88, reduces end-to-end latency by 8–10%, and reduces control overhead ratio (COR) by 10–12%. These improvements result from HHLO’s joint optimization of energy, congestion, mobility, and trust, yielding longer-lived and more reliable networks. By unifying security and optimization within a single framework, HHLO advances the development of sustainable, resilient, and environmentally conscious WSNs for next-generation IoT deployments. Full article

Motivated by the need to reduce failure risks, enhance real-time situational awareness, and support data-driven decision-making, this article comprehensively reviews the latest progress in pipeline safety and intelligent technology, focusing on analyzing the effectiveness and challenges faced by integrity management technology in practical situations. A structured literature survey was conducted to outline the key role and significant achievements of smart technology in improving the efficiency and reliability of pipeline safety management. Using this methodology, the review synthesizes progress in pipeline integrity management and monitoring technology, including the application of distributed strain measurement technology, wireless sensor networks, and Internet of Things technology, as well as the practical effects of deep learning and machine learning in defect detection and incident recognition. Additionally, special attention is given to analyzing the latest achievements in applications of large model technology, distributed optical fiber sensing technology, and acoustic analysis technology in the field of leakage monitoring. Based on the reviewed research, the article identifies key technical challenges, including targeted monitoring technology solutions and management strategies for the challenges in the field of pipeline safety. The findings conclude that intelligent technologies substantially enhance the development trend of AI applications. Hence, next-generation pipeline safety will rely on tightly coupled AI–IoT ecosystems. It anticipates the future of pipeline safety management by providing theoretical reference and technical support for pipeline safety guarantees and intelligent operation and maintenance. Full article

Predictive maintenance based on vibration monitoring can significantly improve gearbox reliability in heavy-industry environments. Although it is well established in vibration engineering that operating regimes influence vibration levels, the contribution of this work lies in providing an integrated, data-driven diagnostic linkage between continuously acquired IoT vibration indicators and key process/operational variables to identify and quantify the dominant drivers of vibration escalation. This study deployed wireless IoT sensors for continuous acquisition of RMS vibration and lubrication temperature in gearboxes operating in cement and mining plants and applied multivariate machine learning models to detect anomalies and identify root causes. We compared boosted multilayer feedforward neural networks, boosted trees, and k-nearest neighbors using RMS vibration and process variables including mill feed, lubrication pressures, and temperature. The boosted neural network delivered superior validation performance and isolated low or near-zero mill feed during operation as the primary driver of elevated RMS vibration, with lubrication instability acting as a secondary interacting factor. This shifts the diagnosis from a generic “high vibration during transients” statement to actionable operational mitigations—minimum feed set-points, controlled ramping logic, and lubrication pressure governance—supported by multivariate evidence. Our results motivate further validation with k-fold and out-of-time tests. Full article

Wireless sensor networks ($WSNs$) are extensively used in $IoT$ applications. Secure access control and data protection are essential. Nonetheless, the wireless environment has an open nature. The limited resources of sensor devices render $WSNs$ susceptible to a variety of security attacks, causing significant difficulties in the design phase of efficient authentication and key agreement ($AKA$) protocols. This study proposes a physically unclonable function ($PUF$)-based lightweight and secure $AKA$ protocol for $WSNs$ based on elliptic curve cryptography ($ECC$). A secure password update scheme is offered, which would allow legitimate users to reset forgotten passwords without re-registration. According to formal security analysis using $BAN$ logic and $ProVerif$, the proposed protocol is secure against common attacks. Moreover, from an entropy perspective, the use of dynamic pseudonyms and fresh session randomness increase an adversary’s uncertainty about user identities, thereby limiting identity-related information leakage. Performance evaluation shows that the proposed protocol achieves lower computational and communication overhead than the existing ones, making it suitable for $WSNs$ with resource constraints. Full article

Structural health monitoring (SHM) has evolved into an indispensable component for ensuring the safety, durability, and life-cycle efficiency of civil infrastructure. Over the past five years, significant technological advancements have been made in innovative sensing systems, facilitating real-time assessment of structural performance and the early detection of deterioration. This comprehensive review presents recent developments in smart sensor-based SHM, with particular emphasis on the convergence of the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) frameworks. Our review critically examines advances in fiber-optic, piezoelectric, MEMS-based, vision-based, acoustic, and environmental sensors, as well as emerging multi-sensor fusion architectures. In addition, bibliometric insights highlight the significant rise in global research activity and influential thematic clusters in SHM between 2020 and 2025. The discussion underscores how AI-integrated data analytics, IoT-enabled wireless networks, and DT-driven virtual replicas enable intelligent, autonomous, and predictive monitoring of bridges, buildings, tunnels, and other large-scale civil infrastructure. Field deployments and case studies are analyzed to bridge the gap between laboratory-scale demonstrations and real-world implementation. Finally, key scientific and practical challenges—including the durability of embedded sensors, the interoperability of heterogeneous data, cybersecurity in connected systems, and the explainability of AI models—are outlined to guide future research. Overall, this review positions contemporary SHM as a transition from traditional damage detection to comprehensive life-cycle management of infrastructure through self-diagnosing, data-centric, and sustainability-driven monitoring ecosystems. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges since dynamic body-shadowing effects and heterogeneous traffic patterns. In this paper, we propose the Nash Bargaining Rate-optimization MAC (NBR-MAC), a hybrid MAC protocol that integrates TDMA-based Guaranteed Time Slots (GTS) with CSMA/CA-based contention access. Unlike traditional schemes, we model the rate allocation as an Asymmetric Nash Bargaining Game, introducing a rigorous disagreement point to guarantee minimum service for critical nodes. The utility function is normalized to resolve dimensional inconsistencies, incorporating sensor priority, buffer status, and channel quality. The Nash Bargaining solution is derived after proving convexity and verifying the axioms. Superframe time slots are allocated based on sensor data priority. Simulation results demonstrate that the proposed protocol enhances transmission success ratio and throughput while reducing packet age and energy consumption under different load conditions. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

In the special case of a heavy-tailed sequence environment, change point monitoring in wireless sensor networks faces many serious challenges, such as high communication overhead, particularly sensitivity to sparse changes, and dependence on strict parameter assumptions. In order to solve these limitations, a distributed robust M-estimator-based change point monitoring (DRM-CPM) method is proposed. This method combines ratio statistics with sliding window technology so that in online detection, there is no need to know the distribution before and after changes in advance. A threshold-triggered communication strategy is introduced, where sensors exchange local statistics only when exceeding predefined thresholds, significantly reducing energy consumption. By means of theoretical analysis, the asymptotic characteristics of the statistics are confirmed, and the robustness of the algorithm to heavy-tail noise and unknown parameters is also proved. Simulation results show that the algorithm is better than the existing methods in terms of empirical size control, empirical power, and communication efficiency, particularly in the face of sparse variation or heavy-tailed data. This framework provides a scalable solution for real-time anomaly monitoring with non-Gaussian data characteristics in industrial and environmental applications. Full article