Search Results (91)

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

High-stakes online assessment produces suspicious-event records, yet storage placement and burst admission remain insufficiently characterized. This article presents a Kubernetes-deployed provenance pipeline integrating Hyperledger Fabric, IPFS, SHA-256 verification, and RabbitMQ ingress. Media objects are retained in IPFS, while compact semantics, CIDs, and verification anchors are committed to Fabric. At 5 TPS, five 300-transaction runs completed under both direct large-payload and compact-anchor ledger conditions. At 20 TPS, compact anchoring of a pre-retained IPFS object sustained 19.40 ± 0.00 TPS with 0.22 ± 0.00 s mean ledger latency; direct large-payload submission achieved 14.20 ± 0.40 TPS with 31.62 ± 3.35 s latency. A write-load sweep identified 100 TPS as the transition point; higher loads were delay-dominated. Three 1000-VU PTS runs reduced mean response time from 4.76 ± 0.78 s to 2.40 ± 0.02 s, with similar failure rates. In three 30-message consumer-enabled runs, all 90 messages reached IPFS retention, Fabric commit, query visibility, and manual acknowledgement without retry, producer failure, or dead-letter outcome. Mean queue-drain and total completion times were 61.80 ± 0.71 s and 62.99 ± 0.15 s. The results isolate Fabric transaction-path payload cost and distinguish queue acceptance from downstream completion. Full article

This paper proposes an operation-index-driven intelligent generation method for distribution network simulation models, integrating model library predefinition, multi-level equivalent modeling, and multi-agent collaboration into an automated pipeline. Three agents collaborate through a unified message bus, task queue, and device model library as follows: Monitor Agent performs time-series data acquisition, national-standard threshold evaluation, and topology verification to trigger modeling tasks; Energy-flow-Model Agent uses a hierarchical model library and LLM (Large Language Model) to automatically match standardized models, parses CIM topology, and applies Thevenin/Ward/three-phase admittance matrix multi-level equivalent modeling to construct Energy Flow Network (EFN) computation graphs; Device-Model-Data Agent supplies missing impedance parameters via a public device standard library. The pipeline automatically generates 15 min granularity current-state assessment models synchronized with operating conditions. Validation on eight consecutive days of 10 kV feeder field measurements (96-time sections/day, 91 transformer areas) shows three-phase voltage mean MAPE of 2.52% and 98.9% correct trigger rate with no false activations or missed detections, achieving end-to-end automation from archive parsing to model generation. Full article

One of the factors that has steadily eroded the legitimacy of public institutions is the recurrence of corruption and irregularities in state management, especially in procurement processes, budget allocation, and public appointments. These phenomena have intensified public mistrust and highlight the urgent need for tools to strengthen transparency in the digital sphere. In this context, e-government platforms have become key mechanisms for promoting openness and accountability in public administration. However, ensuring transparency in electronic procedures remains challenging due to the lack of standardized auditing mechanisms and system interoperability. The objective of this study is to design, implement, and technically evaluate a software architecture for an electronic platform that operationalizes a maturity-based diagnostic model to assess transparency in electronic procedures within public organizations. The proposed platform enables real-time monitoring, centralized data management, automated reporting, and evidence-based transparency assessment. The architecture follows a structured design process aligned with ISO 25010 quality attributes, incorporating microservices, database replication, and load balancing to enhance system performance and security. Performance tests identified bottlenecks in authentication, query processing, and concurrent user load, leading to optimizations such as query indexing, caching, and message queue implementation. The results indicate that a well-structured software architecture enhances government transparency by ensuring auditable, traceable, and secure public data access. The platform provides a self-diagnostic tool for institutions to assess their transparency maturity based on open government principles. Full article

Phishing email detection is a practical requirement for human-operated security and administration workflows, including mail gateways, security operations centre (SOC) triage queues, cloud dashboards, firmware-management portals, and other operational interfaces. In Internet-of-Things (IoT) and wireless sensor network (WSN) operations, compromise of such human-operated interfaces can open an initial-access path into the sensor-network management plane; however, the present study evaluates email body classifiers on public phishing email corpora rather than on-device IoT/WSN hardware. Email filtering must often run on low-cost servers, where running and storing a transformer for every message is costly in compute and memory, and opaque scores are hard to audit. We compare four model families on four public phishing email corpora: hand-engineered classifiers, high-vocabulary classical models, frozen-transformer classifiers, and a CPU-feasible fine-tuned DistilBERT reference. The strongest low-cost model is a Platt-calibrated Linear SVM with high-vocabulary TF-IDF features. It reaches F1 = 0.961 on the primary corpus, slightly exceeds the frozen DistilBERT baseline and is statistically indistinguishable from the fine-tuned DistilBERT reference in-domain, and requires 0.23 MB on disk with about 8 ms per email. This shows that transformer inference is not required for competitive accuracy on the evaluated in-domain corpora. We then evaluate the Calibrated Selective Cascade (CSC) as an operational routing layer: a calibrated low-cost arm handles most messages, while a validation-selected uncertainty band is deferred to a transformer arm. With the strong SVM arm, CSC yields only marginal in-domain F1 gains, but provides a tunable latency/deferral trade-off parameter and a monitorable drift signal. On an exploratory synthetic stress set of stylistically neutral, LLM-style phishing, no evaluated body-only model exceeds F1 around 0.46; closing this gap would require header-, URL-, identity-, and attachment-level signals. Full article

This paper presents high-performance Advanced Vector Extensions 512 (AVX-512) implementations of two Korean standard cryptographic algorithms: the Lightweight Secure Hash (LSH) function and Lightweight Encryption Algorithm–Galois/Counter Mode (LEA-GCM) authenticated encryption. For LSH, we apply three optimization strategies: single-message processing using AVX-512 512-bit vector registers, dual-message parallel processing through register interleaving, and multi-core parallelization using a dynamic queue-based pthread thread pool. For LEA-GCM, we propose an end-to-end optimization that replaces scalar counter-mode (CTR) encryption with 16-block AVX-512 parallel processing and Streaming SIMD Extensions(SSE)-based Galois Hash (GHASH) authentication with VPCLMULQDQ-based four-block parallel processing. Performance evaluation on an Intel Core i7-1165G7 (Tiger Lake) processor shows that LSH-256 achieves an average of 1.16× throughput improvement and LSH-512 achieves an average of 1.61× improvement over the Korea Internet and Security Agency (KISA) AVX2 reference implementation. Dual-message interleaving achieves an average superlinear speedup of 2.28×, driven by instruction-level parallelism (ILP), and the thread pool delivers speedups of 3.73× to 5.13× across eight logical cores (four physical cores with hyperthreading). The optimized LEA-GCM implementation achieves a 3.26× throughput improvement over the KISA SSE-based reference and a 12.1× improvement over the pure software implementation for 4096-byte inputs, with correctness verified against KISA official test vectors. Full article

Controller Area Network (CAN) remains a key in-vehicle communication protocol for distributed automotive control systems, where predictable communication timing is essential for coordinated operation of electronic control units (ECUs). This paper presents a cross-validated framework for timing analysis of automotive CAN networks by combining Deterministic and Stochastic Petri net (DSPN) modeling with worst-case response-time (WCRT) analysis. A DSPN model is developed to represent CAN message generation, priority-based arbitration, bus access, and non-preemptive frame transmission. The model is implemented in TimeNet to evaluate bus utilization, queue occupancy, and access-delay behavior under representative automotive traffic. In parallel, analytical WCRT equations are used to derive conservative latency bounds for each message class. The proposed framework links stochastic performance observations from DSPN simulation with deterministic schedulability guarantees from WCRT analysis, enabling consistency checks between average-case and worst-case timing results. A case study based on a 500 kbit/s automotive CAN configuration with six priority classes is presented. The results show that the network operates at approximately 35.9% bus utilization and that all message classes satisfy their timing requirements with a substantial margin, with the maximum worst-case response time remaining below 2 ms. The study further discusses the modeling assumptions, abstraction limits, and sensitivity of timing behavior to frame length and traffic configuration. The proposed framework provides a practical methodology for timing-oriented design and early-stage validation of automotive CAN communication systems. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article

Centralized agricultural data platforms raise concerns about ownership, provenance, and vendor lock-in, motivating decentralized alternatives. This study evaluates BigchainDB as a blockchain-database hybrid for owner-controlled precision agriculture data sharing. We address three research questions: (1) functional feasibility for data integrity, access control, and heterogeneous sensor integration; (2) integration patterns bridging IoT ingestion with blockchain consensus; and (3) operational trade-offs versus centralized alternatives. A proof-of-concept implementation comprising a sensor simulator, FastAPI middleware, and three-node BigchainDB cluster demonstrates end-to-end data flow with cryptographic provenance. Key contributions include the following: identification of three integration patterns (message queue buffering for high-throughput ingestion, hierarchical asset modeling, and dual-key access control); comparative analysis against five blockchain-database alternatives; and characterization of deployment complexity. Results show BigchainDB satisfies the functional requirements for data integrity and access control, while requiring increased operational overhead compared to single-node databases. The architecture is viable when multi-party governance outweighs operational simplicity, though production deployments require further scalability validation, including detailed performance benchmarking. Full article

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments. Full article

Effectively managing multi-source heterogeneous data remains a critical challenge in distributed cyber-physical systems (CPS). To address this, we present a novel and edge-centric computing framework integrating four key technological innovations. Firstly, a hybrid OPC communication stack seamlessly combines Client/Server, Publish/Subscribe, and P2P paradigms, enabling scalable interoperability across devices, edge nodes, and the cloud. Secondly, an event-triggered adaptive Kalman filter is introduced; it incorporates online noise-covariance estimation and multi-threshold triggering mechanisms. This approach significantly reduces state-estimation error by 46.7% and computational load by 41% compared to conventional fixed-rate sampling. Thirdly, temporal asynchrony among edge sensors is resolved by a Dynamic Time Warping (DTW)-based data-fusion module, which employs optimization constrained by Mahalanobis distance. Ultimately, a content-aware deterministic message queue data distribution mechanism is designed to ensure an end-to-end latency of less than 10 ms for critical control commands. This mechanism, which utilizes a “rules first” scheduling strategy and a dynamic resource allocation mechanism, guarantees low latency for key instructions even under the response loads of multiple data messages. The core contribution of this study is the proposal and empirical validation of an architecture co-design methodology aimed at ultra-high-performance industrial systems. This approach moves beyond the conventional paradigm of independently optimizing individual components, and instead prioritizes system-level synergy as the foundation for performance enhancement. Experimental evaluations were conducted under industrial-grade workloads, which involve over 100 heterogeneous data sources. These evaluations reveal that systems designed with this methodology can simultaneously achieve millimeter-level accuracy in field data acquisition and millisecond-level latency in the execution of critical control commands. These results highlight a promising pathway toward the development of real-time intelligent systems capable of meeting the stringent demands of next-generation industrial applications, and demonstrate immediate applicability in smart manufacturing domains. Full article

Background: Remote health monitoring for time-critical conditions (e.g., acute stroke) demands rapid, reliable data delivery and immediate clinical interpretation. However, existing Remote Patient Monitoring (RPM) frameworks often exhibit fragmented designs, latency bottlenecks, and integration challenges when onboarding new sensors or clinical algorithms. Methods: To address these gaps, we introduce a unified Remote Health Monitoring System (RHMS) that combines MQTT-driven sensor transport, a pattern-oriented software architecture, and blockchain-based immutable audit logging. Results: In a TRL 3–4 technical feasibility evaluation using synthetic load and a 30 min smartwatch trace, RHMS achieved a median end-to-end latency of 480 ms (IQR 110 ms; P95 < 600 ms) under 500 concurrent 1 Hz streams and a peak throughput of 545 streams/s in controlled environments. The system emits algorithmic risk alerts from an integrated model; no adjudicated clinical diagnoses were performed. A modeled rollup-backed audit log estimates a per-record cost of $0.00016 (USD). Conclusion: RHMS demonstrates technical feasibility and interoperability that aligns with clinical recommendations. Clinical validation is out of scope for this study and will require prospective trials. Full article

A context-aware system is a system that adapts its behaviours in response to changes in the system’s environment (i.e., context). Ensuring the correctness of such a system is difficult because the state of the environment changes frequently in an unpredictable manner according to the laws of physics. Hence, formal verification techniques like model-checking and theorem proving do not work in many cases. Runtime Verification (RV) is a lightweight formal verification technique that consists of checking at runtime whether the execution of the system violates the requirements of the system. The Calculus of Context-aware Ambients (CCA) is a process calculus for modelling context-aware systems and reasoning about their behaviours. This paper proposes an RV tool for CCA, called ccaRV. Given a model of a system in CCA and a property of the system written in LTL (Linear Temporal Logic), ccaRV verifies automatically at runtime if the execution of the system violates the property. We propose a semantic approach to RV, where the RV mechanism is defined at the semantics level and not as an add-on. A consequence of this is that there is no need for generating a monitor from the property specification nor for the instrumentation of a system during verification. We define a labelled reduction relation for CCA, where the labels are used to capture the execution traces at the semantics level. Then we extend LTL with spatial operators and context expressions in order to formulate properties about the system context. We use a case study of the MQTT (Message Queue Telemetry Transport) protocol to evaluate the proposed RV approach. The results show that the ccaRV tool is scalable and its decisions are accurate. Full article

The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry Pi with a camera for image-based detection and employed the dark channel prior method to detect the presence of gas. The message queue system was used for the real-time data transmission of gas leak status, temperature, and humidity data. The system sent data to a central server via message queuing telemetry transport (MTQQ). Node-RED was used for data visualization and anomaly alerts. Machine learning methods such as support vector machines (SVMs) and decision trees were applied to analyze the correlation between gas leaks and other environmental parameters to predict leak incidents. This system effectively detected gas leakage and transmitted and analyzed the data, significantly improving the operational efficiency of the gas cylinder filling process. Full article