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The post-quantum signature scheme SQIsign achieves extremely compact public keys and signatures, making it attractive for bandwidth-constrained environments. However, its signing efficiency is limited by the high random failure rate of the ideal-to-isogeny mapping procedure and the substantial cost of each retry. Existing optimizations mainly reduce the number or cost of isogeny computations, while overlooking how to schedule commitment retries when multiple candidate ideals are available. We formulate commitment-stage scheduling as a lightweight learning-to-rank problem and provide an instrumented scheduling framework for SQIsign signing only. The pipeline uses two features, trains a weighted logistic regression scorer offline by maximum likelihood with class weighting, and deploys the same scorer online in Rank-ML mode. Live instrumentation on Apple M2 (n = 20,000 candidate attempts at NIST-I) quantifies the commitment bottleneck (86.4% failure; 7.36 mean attempts per session) and shows constant features at a fixed commitment degree (live AUC $=0.50$). Synthetic training supports the scorer when feature variance is present (test AUC $\approx 0.634$). A remeasured four-way ablation with Batch-only control ($n=100$, seed 42) separates batch overhead from learned ordering: Rank-ML is indistinguishable from Batch-only at deployment, while Baseline remains fastest for its wall-clock signing time at batch size 10. These results clarify when lightweight ML scheduling applies in SQIsign and provide a reproducible evaluation template separating live, synthetic, remeasured, and proxy evidence. Full article

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G networks, where edge-native services are required to satisfy stringent latency, bandwidth, and privacy constraints while operating on highly heterogeneous devices and time-varying wireless channels. In practice, however, synchronous FL is often constrained by straggling clients with limited computation capability or unfavorable communication conditions, which increases round latency and reduces overall resource efficiency. To address this challenge, this study develops a rigorously structured framework for dynamic client selection and radio resource allocation in heterogeneous wireless edge environments. Each FL round is formulated as a latency-aware scheduling problem that jointly captures local computation time, uplink transmission time, minimum participation constraints, and resource block assignment. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that integrates computation-aware, channel-aware, and fairness-aware scoring with greedy resource block allocation guided by marginal completion time reduction. The study further provides a clear methodological structure, workflow visualization, literature-grounded justification, dataset documentation, and uncertainty-aware result reporting. Under the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces the average round completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. Standard deviation reductions of 70.59% and 80.77% further indicate improved round-to-round stability and more reliable training behavior. These results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks. Full article

The internet of things (IoT) has rapidly evolved into a ubiquitous communication paradigm for enabling the deployment of autonomous wireless networks across diverse application domains. However, the limited energy storage capacity and computational resources of IoT devices (IoTDs) pose a serious concern to their long-term sustainability and the expected quality of service delivery. Moreover, in the foreseeable era of the internet of everything, centralised network resource management is likely to constrain network scalability. To tackle these challenges in the current and next-generation communication networks, the adoption of adaptive and lightweight computational frameworks coupled with energy-efficient transmission strategies is essential. To demonstrate this, we exploit the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs. Furthermore, to intelligently and autonomously perform resource allocation, we employ the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning. Based on key performance evaluation metrics, we compare our findings with the baseline methods, including the equal, random, and greedy power level selection schemes, with SARSA exhibiting the most favourable performance trade-offs. Full article

IoT networks now handle traffic from billions of devices, and edge nodes are under constant pressure to classify that traffic and dispatch tasks within tight latency deadlines. Most existing systems treat classification and scheduling as two separate steps that run one after the other. This sequence adds unnecessary delay and breaks the feedback between the two tasks: the scheduler never sees the traffic type, and the classifier never sees the queue state. We propose ParallelEdge-AI, a system built around a shared flow encoder that feeds two task-specific heads in parallel, one for multi-class traffic classification and one for task-urgency scoring. Both heads are trained end-to-end using a joint loss that combines cross-entropy and pairwise ranking. A load-balance controller then reads the urgency scores alongside live queue lengths to decide, every 200 ms, whether a task stays local or moves to a less-loaded edge node. No global synchronisation is needed. We test the system on three real IoT datasets: RT-IoT2022, N-BaIoT, and CICIoT2023. ParallelEdge-AI reaches 97.63% accuracy and an F1-score of 97.34%, which is 3.16 percentage points above the best baseline. Inference latency is 19.62 ms per batch, the deadline-miss rate is 2.34%, and the load-imbalance index is 0.083, all three are the best results in our comparison. These numbers show that running classification and scheduling together on a shared representation is both faster and more accurate than treating them as separate problems. Full article

Long-Range Wide-Area Networks (LoRaWANs) combine long-range and low-power communication, making them a key technology for Internet of Things (IoT) applications. This systematic mapping study provides a comprehensive analysis of research on LoRaWAN network technology, focusing on performance and robustness optimization published between 2015 and 2026. Through a rigorous screening of2746 papers, we identified and analyzed 209 papers that met strict inclusion criteria and addressed network-layer optimization mechanisms. The studies were retrieved from IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus using a PICO-based search strategy, and synthesized descriptively without effect-size meta-analysis. Our analysis reveals a rapidly growing research field, with 53.1% of the 209 included studies were published in the recent period (2023–2026), predominantly simulation-based evaluation approaches (72.2%), and strong geographic concentration in Europe (38.8%) and Asia (35.4%). We identified that performance optimization is the primary focus (96.2% of papers), while robustness optimization remains significantly underfocused (27.3% of papers), representing a critical research gap. This study identifies and prioritizes five research gaps, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices. This mapping study provides structured guidance for future research in LoRaWAN optimization and supports evidence-based decision-making in the field. Full article

Firewall rule ordering aims to improve packet filtering efficiency while preserving the dependency constraints that guarantee the intended security behavior of the policy. Existing approaches often rely either on local criteria, such as rule frequency, or on iterative optimization procedures whose behavior depends on initialization, parameter settings and search budget. In this paper, we propose PPCO, a deterministic dependency-aware rule ordering method based on propagated probability combined with descendant-based centrality. The proposed score reflects both the traffic relevance of a rule and its structural influence in the dependency graph. The structural component is essential, especially when some rules are inactive or have zero activation probability, since it prevents probability-based ties from violating dependency constraints. The final policy is obtained directly by sorting rules in a decreasing score order. Experiments were conducted on synthetic rule sets ranging from 50 to 2000 rules and on ClassBench-ng benchmark instances, showing that PPCO consistently achieves a competitive ordering quality among the compared deterministic methods under the considered experimental settings. The method remains stable as the policy size and dependency rate increase, produces zero dependency violations in all valid configurations, achieves the lowest score-coherence values, and maintains competitive execution times at large scales. These results suggest that PPCO provides an effective, robust, and computationally efficient solution for dependency-aware firewall rule ordering within the scope of the evaluated configurations. Full article

Path loss modeling is essential for the design, analysis, and applications (e.g., localization) of low-power wide-area networks (LPWANs). Conventional models typically rely on coarse regional land cover classifications (e.g., urban or suburban), which fail to capture the direction-dependent path loss variations of long-range LPWAN links that traverse heterogeneous environments. Although per-link modeling and geographical clustering have individually shown promise in addressing these limitations, their combined potential remains unexplored. This paper presents GeoSeg, a path loss modeling approach that integrates per-link modeling with geographical clustering. GeoSeg represents the propagation environment between each transmitter-receiver pair as a variable-length sequence that encodes both land cover types and their spatial arrangement and employs a hidden Markov model (HMM)-based clustering method to group these sequences into subregions. A per-subregion path loss exponent is then estimated for each identified subregion, enabling spatially adaptive path loss estimation. Evaluated using an open-access LoRaWAN dataset, the preliminary results demonstrate median MAE reductions of up to 96% across the evaluated clusters compared with the standard log-distance path loss model. These results suggest that integrating per-link environmental characterization with geographical clustering can potentially improve path loss estimation accuracy in heterogeneous LPWAN deployments. Full article

The deployment of 5G technology represents a significant advancement in telecommunications, offering unprecedented speed, connectivity, and innovation opportunities. However, this progress comes at a significant cost for Public Land Mobile Network (PLMN) operators, who face challenges in meeting high Quality of Service (QoS) standards for optimal user experience while ensuring appropriate levels of security. This paper addresses the joint optimization of latency and resource consumption under security constraints within 5G networks, focusing on the Packet Data Unit (PDU) session path to ensure compliance with security and latency requirements. We propose an innovative approach in which access control rules are distributed across User Plane Functions (UPFs) in the network. The optimization problem has been formulated as a mixed integer linear programming (MILP) problem that aims to minimize round-trip latency and operational costs for PLMN operators. We evaluate the performance of our model using a discrete event network simulator (NS3). The simulation results demonstrate the effectiveness of our approach, particularly in scenarios with stringent latency requirements. Latency is reduced, and a lower session drop rate is maintained, especially in conditions of network congestion. These findings emphasize the importance of considering both QoS and security in the design of next-generation 5G networks. Full article

Secure and trustworthy data exchange across distributed data sources remains a major challenge in the health domain, where strict legal, regulatory, and privacy requirements must be satisfied. Data space technologies provide a promising approach to enabling interoperable and sovereign data sharing among diverse stakeholders while preserving data ownership and regulatory compliance. The NextGEM Innovation and Knowledge Hub (NIKH) was developed as a collaborative ecosystem for FAIR data access and evidence-based health risk assessment. This paper describes the NIKH Data Space (NIKH-DS), the underlying network provisioning platform within NIKH that enables secure data exchange in a health data space environment. The work outlines the key requirements, intended uses, and core implemented functionalities necessary for enabling secure network-provisioned data sharing among distributed data locations. Based on these requirements, a prototype architectural framework is proposed that integrates secure networking and interoperable services. The implementation of the individual components is described, including the data space controller, access control mechanisms, and a user-oriented dashboard that enables data visualization and interaction with distributed data sources. The NIKH-DS platform is validated through a set of case studies that demonstrate the feasibility and effectiveness of the platform in supporting secure, interoperable, and Findable, Accessible, Interoperable and Reusable (FAIR)-compliant health data sharing and risk assessment for the investigation of potential health effects of radio-frequency electromagnetic fields (EMF). Full article

Cyber-physical smart infrastructures integrate sensing devices, communication networks, control components, and service platforms, which makes them vulnerable to malicious activities that may evolve gradually through several attack stages. The objective of this study is to develop and evaluate a simulation-based cybersecurity framework capable of detecting a proposed novel multi-stage cyber attack and identifying its internal progression within a realistic smart infrastructure environment. To achieve this objective, a NetSim-based cyber-physical smart infrastructure was modeled to generate both normal operational traffic and staged malicious traffic. The generated traffic was captured, processed, labeled, and transformed into a stage-aware cybersecurity dataset. An artificial neural network (ANN) model was then trained and evaluated for two detection tasks: binary classification of normal versus attack traffic and multi-class classification of compromise, coordination, and execution attack stages. Twenty experimental configurations were designed to examine the model under progressively broader infrastructure contexts, including sensing, service, gateway, control, backbone, and full-span operational scenarios. The best binary testing performance was achieved in the eighteenth experimental configuration, representing a broad full-span infrastructure scenario, with 97.96% accuracy, 97.80% precision, 97.65% recall, 97.72% F1-score, and 1.06% false positive rate. For stage-aware multi-class detection, the ANN model achieved 96.97% accuracy, 96.36% macro-averaged precision, 96.20% macro-averaged recall, 96.28% macro-averaged F1-score, and 96.55% weighted F1-score. Macro-averaged metrics report the unweighted average performance across classes, while weighted F1-score accounts for class support. These results show that the proposed simulation-based framework can generate realistic attack-aware traffic data and support reliable ANN-based detection of both attack presence and attack-stage progression. Full article

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives. Full article

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state. Full article

This paper presents an organization-managed IMAP Agent framework for extending email functionality in environments that rely on outsourced mail services. In this study, outsourced mail services refer to externally operated mailbox providers offering sufficiently scalable email infrastructures and standard IMAP interfaces, such as Gmail, Microsoft 365, and other commercial mailbox providers. In the proposed framework, IMAP Agents are operated within an organization, while user authentication continues to rely on existing institutional infrastructures such as Identity Providers (IdP) or Integrated Authentication Infrastructure (IAI). The IMAP Agent operates as a post-authentication processing component using credentials issued by these infrastructures, without modifying or intervening in the outsourced mail service itself. The framework enables organization-managed mailbox-side email processing without requiring administrative control over the mail server or dependence on provider-specific APIs. As a proof of concept, representative email-processing functions are implemented, including detection of suspicious messages based on header-level authentication information and automatic insertion of thread-consistent warning messages without altering the original email content. To evaluate the feasibility of the proposed framework, a prototype system was implemented using multiple containerized IMAP Agent instances. The experimental results showed that warning messages were typically appended within approximately 300 ms after message detection. Multi-container evaluations ranging from 1 to 100 concurrent IMAP Agent instances demonstrated low CPU overhead and approximately linear memory growth under idle-monitoring conditions, indicating the operational feasibility of deploying multiple IMAP Agent instances on a single host. These results suggest that the proposed framework can provide provider-independent and organization-managed extension of email functionality in outsourced mail environments through standard IMAP operations. Full article

Network function virtualization (NFV) enables cost reduction and optimized service deployment. By means of virtualization, network functions which used to be executed on specialized hardware are being replaced with software called Virtual Network Functions (VNFs) that can run on commodity hardware. These VNFs are applied to data flows passing through network nodes with VNFs hosted on them. To fully realize the benefits of NFV, each flow must be fully processed on VNF nodes. Given the budget constraints, only a finite number of nodes can be selected to host VNFs, and these nodes also have limited capacity to process the flows passing through them. In this paper, we consider the problem of VNF node placement and capacity allocation in a network graph $G=(V,E)$, i.e., selecting the best subset of VNF nodes and optimally distributing their bandwidth to maximize the total volume of fully processed traffic flows F. We propose a simpler algorithm for solving this problem than the previously proposed version, representing it as an integer linear programming problem with an approximation ratio of ${\displaystyle \frac{1}{2}}(1-1/e)$, and time complexity $O(|V{|}^{2.5}·|F{|}^{2.5}·L)$, where L is the number of bits of input data. Full article

The 5G Wireless and Wireline Convergence (5G-WWC) standards introduce critical network functions—notably the Access Gateway Function (AGF) and the User Plane Function (UPF)—to enable unified wired and wireless access through a single 5G core. However, deploying and integrating these functions across heterogeneous programmable hardware platforms remains a significant open architectural challenge. This paper presents a configurable integration framework that orchestrates AGF and UPF workloads on heterogeneous programmable data planes, specifically NVIDIA BlueField-2 Data Processing Units (DPUs) and P4-based switches. Unlike traditional, hardware-specific implementations, the framework provides a unified control plane that dynamically manages AGF-only, UPF-only, or Combined AGF/UPF deployments. A hardware abstraction mechanism decouples the control logic from pipeline-specific details, enabling the same control plane to drive different underlying hardware without modification. A Generic Flow Rule interface standardises communication between the control plane and each user-plane backend, while a merged DPU pipeline for Combined AGF/UPF eliminates the redundant GTP-U encapsulation and decapsulation steps inherent in a naively cascaded design. Experiments on NVIDIA BlueField-2 DPUs achieve near-100 Gbps throughput across all three TR-470 scenarios (AGF-only, UPF-only, and Collocated AGF/UPF). The Combined AGF/UPF configuration exhibits lower end-to-end latency than the separated AGF + UPF configuration, confirming both the feasibility and the efficiency of the proposed framework for next-generation high-performance programmable networks. Full article

Vehicular Ad Hoc Networks (VANETs) face significant challenges in high-mobility environments, where dynamic channel conditions, particularly Doppler Shift (DS), degrade communication reliability and increase latency, thereby undermining safety-critical applications. To address these limitations, this paper proposes a neural network (NN)-based link adaptation strategy trained using the Scaled Conjugate Gradient (SCG) algorithm. SCG is selected as a second-order approximation optimizer that leverages curvature information to produce well-conditioned weight updates particularly suited to the small, physics-constrained training dataset. The SCG-optimized model dynamically adjusts transmission parameters to mitigate DS effects, improving real-time adaptability by explicitly incorporating Doppler Shift as a key input feature. Simulation results demonstrate that the proposed approach outperforms both the conventional Auto Rate Fallback (ARF) method and the SampleRate baseline. Specifically, the SCG-based strategy achieves an overall throughput improvement of +34.6% relative to ARF (1.77 Mbps vs. 1.32 Mbps) across all tested conditions, with condition-specific gains of +16.1% at 5 Hz Doppler (0.9 km/h), +21.7% at 750 Hz (137.3 km/h), and +35.2% at 1500 Hz (274.6 km/h), while consistently reducing transmission duration. A formal ablation study confirms that the Doppler Shift feature alone contributes +67% to +78% throughput gain at high mobility (DS > 900 Hz) compared to an SNR-only model. The main contributions of this work are threefold: (i) the explicit integration of Doppler Shift as a first-class input feature for link adaptation; (ii) the application of SCG optimization for fast, stable training of a lightweight feedforward neural network on a compact, physics-constrained dataset; and (iii) the formal ablation study that isolates and quantifies the Doppler feature’s contribution, establishing that the performance gain is attributable to feature engineering rather than the neural network architecture alone. This approach offers a scalable, real-time solution for Doppler-resilient VANET link adaptation. Full article

A coupled multi-stage learning framework is presented for joint bit error rate (BER) prediction and beam angle optimization in massive multiple-input multiple-output (MIMO) systems under a controlled simulation protocol. Unlike purely sequential benchmarking pipelines, the proposed method jointly coordinates BER prediction and beam-angle selection through a shared latent representation, an uncertainty-guided refinement mechanism, a cross-stage consistency loss and alternating optimization. Ten diverse approaches are systematically evaluated across two task-specific stages: Stage 1 examines six classical and adapted methods for BER prediction, including polynomial regression and deep unfolding networks; Stage 2 investigates four machine-learning and generative adversarial network (GAN)-based approaches for angle optimization, including conditional GANs and the proposed Direct-Angle neural network. Stage 3 couples the best-performing methods into a unified hybrid architecture through a shared encoder, explicit consistency regularization and alternating cross-stage updates, thereby producing an integrated beamforming decision strategy rather than an independent cascade. It is shown through the evaluation that the coupled hybrid framework achieves 96.0% overall angle-selection accuracy, a mean BER of $8.0\times {10}^{-5}$ and 100% BER tolerance compliance within $\pm 3$ dB. In this framework, a differentiable BER surrogate initialized from a second-degree polynomial-regression teacher is coupled with the proposed Direct-Angle-NN for angle optimization. Relative to the strongest reimplemented literature baseline under the same controlled simulation assumptions, a 33.3% reduction in mean BER is achieved. Ablation experiments show that the coupling mechanism provides a modest but consistent improvement over the decoupled sequential baseline, increasing angle-selection accuracy from 93.5% to 96.0% and reducing mean BER from $1.05\times {10}^{-4}$ to $8.0\times {10}^{-5}$; the shared encoder accounts for the largest part of this gain while the consistency loss adds 0.6 percentage points. These results indicate that the shared encoder, consistency regularization and uncertainty-guided refinement improve the final beamforming decision, although the gain should be interpreted as incremental rather than as a large architectural breakthrough. A spectral efficiency of 38.0 bps/Hz and an energy efficiency of 0.466 Gbps/W are achieved with a power consumption of only 32.6 W. The theoretical discussion is presented as an analytical characterization of BER sensitivity, complemented by a computational-complexity assessment and empirical convergence diagnostics for the alternating optimization, rather than as a formal optimality proof. The effectiveness of the framework across multiple performance metrics is supported by Monte Carlo simulations, while the limitations of the current setup, including perfect CSI, uncoded QPSK, ideal hardware assumptions and a fixed beam codebook, are explicitly discussed. The complete simulation framework, including code and trained models, can be made available by the corresponding author upon reasonable request to facilitate reproducible research in massive MIMO optimization. Full article

Weather forecasting, given the ever-increasing occurrence of climate change-induced events, has been widely introduced as a method to offer accurate and timely forecasts for proactive measures and risk mitigation. Artificial intelligence of things (AIoT) offers promising solutions for short-term weather forecasting, contributing to the advancement of sustainable and efficient weather monitoring technologies. This work presents everWeather_2.0, a significantly enhanced low-cost and self-powered AIoT-based weather forecasting station, which addresses key challenges in power consumption, user engagement and forecasting accuracy. The proposed end-to-end Cloud-Edge-IoT (CEI) proof-of-concept solution improves upon its predecessor by combining a more robust renewable energy subsystem for complete power autonomy with a series of lightweight, adaptive statistical models for on-device forecasting and an integrated display for on-site user engagement. Deployed in a real-world scenario, the station demonstrated seamless operation and high short-term forecasting accuracy for the thermodynamic variables during the pilot deployment period, with model errors observed as low as 2% for 30 min forecasts to 4.3% for 120 min intervals, validating its applicability in real-time and continuous physical weather monitoring. While wind speed and rainfall were monitored, they were excluded from the current accuracy metrics due to their high volatility and the insufficient number of events recorded during the pilot period to ensure reliable modeling. Full article

Over the last two decades, the automotive industry has spearheaded a shift toward data-centric manufacturing, where Real-Time Ethernet (RTE) networks defined in IEC61784-2 serve as critical components for ensuring deterministic communication at the Operation Technology level. Although AI-based systems offer significant potential for predictive maintenance and cybersecurity, their effectiveness is currently limited by a lack of structured datasets from real-world industrial environments. Most existing research relies on small-scale simulations or laboratory setups that fail to capture the scale and complexity of actual production. To address this gap, this paper introduces a novel methodology for repurposing network data collected throughout a plant’s lifecycle, specifically during the commissioning and validation phases of RTE networks according to IEC61918. An additional important contribution is the creation of the first multi-plant dataset for real RTE (PROFINET) traffic in the automotive sector, aggregating 300 GB of data from 54,000+ devices across nearly 700 production lines in 17 industrial sites. The work defines standardized methodologies and replicable processes for systematic data acquisition, validation, and labeling to ensure long-term usability for training AI models. Finally, four case studies (focused on performance, maintenance, security, and machine learning) show how this dataset can be used to enhance the reliability of modern smart manufacturing. Full article