Search Results (101)

The growing complexity, age, and environmental exposure of civil infrastructure assets—bridges, tunnels, buildings, highways, and dams—have necessitated a transition from reactive or preventive maintenance strategies toward predictive, data-driven systems. The integration of IoT and Digital Twin (DT) technologies provides a transformative paradigm for intelligent monitoring, early fault detection, and real-time lifecycle management. This paper explores the technological convergence of IoT sensor networks, edge-cloud analytics, and digital twin platforms for predictive maintenance in civil and structural engineering. The study presents a multi-layered DT–IoT integration framework designed for infrastructure assets, emphasizing interoperability, cybersecurity, and semantic data synchronization. Key research outcomes include enhanced asset availability, reduced maintenance costs, and improved safety margins. The proposed architecture incorporates sensor-level digital shadows, edge inference modules, and cloud-based analytical twins powered by hybrid machine learning and finite element models. Real-world applications and case studies from smart bridges and intelligent building systems demonstrate prediction accuracies exceeding 90% in identifying early structural fatigue indicators. Ultimately, the results underscore the strategic role of DT–IoT convergence in realizing sustainable, resilient, and self-aware civil infrastructure aligned with Industry 5.0 principles. This study provides a roadmap for digital transformation in asset management, integrating standards such as International Organization for Standardization (ISO) 23247 and ISO 19650 to ensure interoperability and lifecycle traceability. The results reinforce that predictive maintenance through DT and IoT integration is not only technically viable but essential for extending infrastructure lifespan, minimizing unplanned downtime, and achieving carbon-efficient asset operation. Full article

Low-Power Wide-Area Networks (LPWANs) like LoRaWAN enable IoT applications with low-power and long-range characteristics. While LoRaWAN class B mode is server-initiated downlink communication-oriented, its uplink communication, especially in mobile scenarios, remains underexplored. This paper proposes two novel systems, Edge-based SmartSync and Gateway-based SmartSync, aiming to enhance uplink by leveraging class B synchronization. Edge-based SmartSync enables end devices to dynamically adjust the Spreading Factor (SF) based on real-time Received Signal Strength Indicator (RSSI) from beacons, achieving a significant improvement in terms of packet delivery and energy consumption. Gateway-based SmartSync ensures the fair distribution of end devices across a lower SF to further enhance the efficiency of the system. The beacon is reengineered to convey sensitivity limits to end devices. The systems were implemented in the OMNeT++ simulator over a 25 km² area with 100–1000 mobile devices and evaluated against a baseline using metrics like the Packet Delivery Ratio, collisions, and energy consumption. The obtained results show that both systems are capable of improving the delivery ratio by over 40% and reducing collisions by 80% compared to the baseline, with energy savings exceeding 35%. Proposed systems offer cost-effective, adaptable solutions, paving the way for more reliable IoT deployments. Full article

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built. Full article

The development and validation of a comprehensive five-phase methodology for creating a functional digital twin of complex educational infrastructures are presented, implemented through the IPN Hidalgo campus as a case study. Unlike conventional approaches that focus on isolated aspects of digital twin development, this integrated methodology systematically addresses the complete lifecycle from physical characterization to operational synchronization. The implementation resulted in an interactive digital twin integrating 15 buildings and over 200 network components, deployed across multiple platforms, including: desktop, mobile, and mixed reality devices. The validation results demonstrated a 30% reduction in fault identification time for technical teams and 85% user satisfaction regarding interface intuitiveness, with instrument reliability confirmed by a Cronbach’s alpha coefficient of 0.78. The methodological framework establishes a reproducible standard for developing educational digital twins that combine geometric accuracy with dynamic operational capabilities, offering significant advantages over fragmented approaches reported in the literature. Furthermore, the digital twin serves as a foundational platform for future integration of Internet of Things (IoT) sensors and predictive analytics, aligning with emerging trends in educational infrastructure management through immersive technologies. Full article

Real-time processing demands for massive IoT sensor data necessitate reliance on distributed microservice systems within edge clusters. However, pinpointing the root cause of anomalies within these edge microservice clusters poses a critical challenge for intelligent IoT operation and maintenance. To address the issue, a spatio-temporal graph propagation model ST-GraphRCA is proposed for root cause analysis in IoT edge environments. Our approach begins by resolving the fundamental issue of time-series asynchrony across distributed multi-source metrics. A PCA-DTW hybrid feature extraction method is introduced with a dynamic alignment strategy to mitigate the effects of random network delays and data deformation without requiring prior synchronization. Subsequently, ST-GraphRCA constructs a stream-based forward propagation graph based on the flow conservation principle. By integrating dynamic edge weights with node-level input–output anomaly scores, ST-GraphRCA precisely infers fault propagation pathways and identifies potential root cause candidates through causal reasoning. Finally, a topology-constrained high-utility mining algorithm filters these candidates. Using a constraint matrix, the algorithm filters out unreachable service combinations to locate low-frequency and high-risk root causes. Experimental results indicate that ST-GraphRCA achieves an F1-Score of 0.89, outperforming existing methods. In resource-constrained edge scenarios, its average localization time is merely 238.8 ms, representing a six-fold improvement over key benchmarks. Thus, ST-GraphRCA not only provides an efficient anomaly fault tracing solution for large-scale IoT systems but also offers technical support for the intelligent operation and maintenance of distributed microservice systems. 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

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage. 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

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential. Full article

The Loop-Back Quantum Key Distribution (LB-QKD) protocol establishes a bidirectional architecture in which a single photon travels forth and back through the same optical channel. Unlike conventional one-way schemes such as BB84, Alice performs both state preparation and measurement, while Bob acts as a passive polarization modulator and reflector. This design eliminates detectors at Bob’s side, minimizes synchronization requirements, and enables compact, low-power implementations suitable for quantum-mobile and IoT platforms. An extended three-basis configuration $\{X,Y,Z\}$ is introduced, preserving the simplicity of the two-basis scheme while improving noise tolerance through enhanced orthogonality-based filtering. Analytical modeling shows that the effective protocol error decreases from $E_{\mathrm{protocol}}^{\left(2\right)}=e/2$ to $E_{\mathrm{protocol}}^{\left(3\right)}=e/3$, achieving a 33% improvement in noise resilience. Despite its slightly lower sifting efficiency ($\eta =1/6$), the total information gain reaches $G=0.26$ bits per pulse, maintaining post-sifting throughput comparable to BB84. The protocol doubles the tolerable QBER of conventional QKD, sustaining secure operation up to $22\%$ for two bases and approximately $47.58\%$ for three bases. Its passive, self-verifying architecture enhances resistance to man-in-the-middle, photon-number-splitting, and side-channel attacks, providing a scalable and energy-efficient framework for secure key distribution and authentication in next-generation mobile and distributed quantum networks. Full article

The proliferation of power converters in modern energy production systems has led to increased harmonic content due to the commutation of active switching devices. This increase in harmonics contributes to lower system efficiency, reduced power factor, and consequently, a higher reactive power requirement. To address these issues, this paper presents both simulation and experimental results of various control strategies implemented on Parallel Voltage Source Inverters (PVSI) for harmonic mitigation. The proposed control strategies are categorized into direct and indirect control methods. The direct control techniques implemented include the instantaneous power method (PQ), the synchronous algorithm (DQ), the maximum principle method (MAX), the algorithm based on synchronization of current with the voltage positive-sequence component (SEC-POZ), and two methods employing the separating polluting components approach using a band-stop filter and a low-pass filter. The main innovation in these active power filter (APF) control strategies, compared to traditional or existing technologies, is the real-time digital implementation on high-speed platforms, specifically FPGAs. Unlike slower microcontroller-based systems with limited processing capabilities, FPGA-based implementations allow parallel processing and high-speed computation, enabling the execution of complex control algorithms with minimal latency. Additionally, the enhanced reference current generation achieved through the seven applied methods provides precise harmonic compensation under highly distorted and nonlinear load conditions. Another key advancement is the integration with Smart Grid functionalities, allowing IoT connectivity and remote diagnostics, which enhances system monitoring and operational flexibility. Following validation on an experimental test bench, these algorithms were implemented and tested on industrial APF prototypes powered by a standardized three-phase network supply. All control strategies demonstrated an effective reduction in total harmonic distortion (THD) and improvement in power factor. Experimental findings were used to provide recommendations for choosing the most effective control solution, focusing on minimizing THD and enhancing system performance. Full article

Automated monitoring systems increasingly leverage diverse sensing sources, yet a disconnect often persists between machine vision and IoT sensor pipelines. While IoT sensors provide reliable point measurements and cameras offer rich spatial context, their independent operation limits coherent environmental interpretation. Existing multimodal fusion frameworks frequently lack tight synchronization and efficient cross-modal learning. This paper introduces a unified edge–cloud framework that deeply integrates cameras as active sensing nodes within an IoT network. Our approach features tight time synchronization between visual and IoT data streams and employs cross-modal knowledge distillation to enable efficient model training on resource-constrained edge devices. The system leverages a multi-task learning setup with dynamically adjusted loss weighting, combining architectures like EfficientNet, Vision Transformers, and U-Net derivatives. Validation on environmental monitoring tasks, including classification, segmentation, and anomaly detection, demonstrates the framework’s robustness. Experiments deployed on compact edge hardware (Jetson Nano, Coral TPU) achieved 94.8% classification accuracy and 87.6% segmentation quality (mIoU), and they also sustained sub-second inference latency. The results confirm that the proposed synchronized, knowledge-driven fusion yields a more adaptive, context-aware, and deployment-ready sensing solution, significantly advancing the practical integration of machine vision within IoT ecosystems. Full article

Forest fire monitoring is of great significance for ecological protection and public safety. This study proposes a monitoring technology based on LoRa-Mesh (Long Range-Mesh) wireless communication, integrating temperature and humidity sensing, image acquisition, fire identification, data transmission, and energy-saving optimization. To address the limitations of traditional LoRa networks in flexibility and energy consumption, a Layered Dynamic Synchronization Energy-saving (LDSE) protocol is designed. By constructing a hierarchical network, employing implicit route exploration, multi-channel and multi-path communication, and time synchronization optimization, the protocol significantly reduces packet loss rate and system energy consumption. Experimental results demonstrate that the LDSE protocol outperforms the traditional Ad hoc On-Demand Distance Vector Routing Protocol (AODV) in terms of packet loss rate, energy consumption, and latency. Additionally, the proposed energy-saving algorithm significantly reduces system power consumption, with the node sleep-relay mode exhibiting optimal energy efficiency. Experimental verification confirms that the system achieves high reliability, low power consumption, and efficient data transmission, providing an effective IoT solution for forest fire prevention. Full article