WS-STACK: A Weighted Stacking Ensemble with Multi-Criteria Feature Selection for Multi-Class Traffic Classification and Anomaly Detection in Heterogeneous Wireless Sensor Networks

Zainab Hussein; Nureize bt

doi:https://doi.org/10.54216/IJWAC.100206

International Journal of BIM and Engineering Science | 2026

BIM-Integrated Semantic Risk Intelligence for Construction Safety Severity Prediction Using Incident Narratives and 4D Work-Zone Attributes

Construction safety management increasingly depends on the ability to connect static building information models with dynamic evidence from site operations. This paper proposes a BIM-integrated semantic risk intelligence model that translates accident narratives into work-zone risk indicators and uses them to infer safety severity. The model links textual incident evidence with BIM-relevant descriptors, including construction phase, spatial zone, temporary protection status, energy isolation, and proximity to safety constraints. A formal risk-scoring layer is combined with supervised severity learning to provide interpretable decision support for safety planning and 4D coordination. The study contributes a reproducible methodology for converting unstructured safety reports into BIM-actionable risk representations, supporting early hazard prioritisation, design-for-safety review, and site control planning. The findings indicate that semantic evidence becomes more useful when it is explicitly fused with BIM phase and spatial context, rather than being treated as disconnected textual data.

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Esam El-Mekawy mail

link https://doi.org/10.54216/IJBES.120205

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

BIM Integration Across Engineering Disciplines: A Systematic Review of Methodological Advances, Interoperability Challenges, and Emerging Digital Frameworks

This paper provides a comprehensive systematic review of Building Information Modeling (BIM) integration across ten engineering disciplines, synthesising publications from January 2020 to January 2026. It identifies convergent trends, persistent knowledge gaps, and translational barriers that separate research prototypes from scalable industry practice. A PRISMA-guided systematic review was conducted across Scopus, Web of Science, ASCE Library, and ScienceDirect. An initial corpus of 4,712 records was screened and quality-assessed, yielding 63 papers for quantitative synthesis and a broader qualitative corpus of 293 studies spanning ten sub-domains: BIM–digital twin integration, BIM and artificial intelligence/machine learning, interoperability and IFC, structural engineering, MEP and building services, facility management and operations, BIM–GIS for smart cities, off-site and modular construction, adoption barriers, and energy and sustainability analysis. Annual BIM publications grew by approximately 256% between 2019 and 2024. BIM–AI/ML and BIM–digital twin integration are the two fastest-growing sub-domains, yet both remain constrained by data standardisation deficiencies and a shortage of domain-specific training datasets. IFC-based interoperability has matured significantly, but real-time bidirectional exchange across disciplines remains nascent. Structural engineering applications exhibit the highest technology readiness, while BIM–GIS integration for smart-city applications shows the widest gap between published prototypes and commercial deployment. The review delivers a thematic roadmap and a consolidated evidence base for prioritizing investment in digital workflows, standards development, and workforce training. An original four-layer integrated framework is proposed that connects engineering code provisions, AI/ML analytics, digital twin synchronisation, and automated quantity extraction within a single traceable workflow.

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Ann Wolter mail -

Paul Bailey mail -

Raja Ahmed Hassan mail -

Wipitha Mazungwi mail

link https://doi.org/10.54216/IJBES.120206

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2026

Machine Learning-Driven Cyber Threat Prediction and Prevention: A Multi-Dataset Design and Comparative Evaluation

As technology advances, the frequency and variety of intrusions and other security threats within network environments continue to grow. Intrusion detection systems (IDS) play a vital role in securing networks against unauthorized access and attacks on computer systems; however, traditional IDSs are very limited in their ability to recognize new, complex malicious threats because they rely on signature-based detection. Approaches based on machine learning have shown a promising alternative in identifying unknown malicious attacks. This study proposes a computationally efficient, generalizable machine-learning framework for robust cyber-threat prediction. Three benchmark datasets (HIKARI-2021, CIC-IDS2017, and KDDCup99) were used for full-pipeline evaluations, including preprocessing, feature selection, class-imbalance handling, hyperparameter optimization, and strict model validation. Eight classifiers were assessed, which included traditional classifiers and more modern ensemble methods. The results from this study showed that tree-based models, mainly both Random Forest and XGBoost achieved near-perfect performance across all datasets, reaching accuracy values up to 0.999 and F1-scores between 0.99 and 0.999. Additionally, the SHAP-based explainability analysis was applied to reveal features that drove predictions, enabling interpretability and transparency. Compared with prior studies, the proposed framework consistently delivers improved, more stable detection performance. The findings highlight that optimized ML models combined with balanced datasets and rigorous validation protocols can significantly enhance intrusion detection reliability. Furthermore, this approach provides a practical and scalable solution for strengthening cybersecurity defenses against evolving and emerging cyber threats.

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Krishneel Sundar mail -

Pritika Reddy mail -

Kaylash C. Chaudhary mail

link https://doi.org/10.54216/JCIM.180106

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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International Journal of Wireless and Ad Hoc Communication | 2026

TA-FaultNet: A Temporal Attention Framework with Bidirectional LSTM for Multi-Class Fault Detection and Health Monitoring in Industrial Wireless Sensor Networks

Industrial wireless sensor networks are central to the continuous monitoring of critical plant equipment, yet reliable identification of multiple concurrent fault modes from heterogeneous multivariate sensor streams remains an unsolved operational challenge. Physical failure mechanisms—pump cavitation, valve blockage, gradual sensor drift—and wireless channel disturbances each imprint distinct but overlapping temporal signatures that render classical thresholdand rule-based detectors inadequate for automated maintenance dispatch. This paper  presents TA-FaultNet, a neural architecture designed specifically for the multi-class fault identification problem in industrial sensor deployments. The network couples a two-stage stacked bidirectional recurrent encoder with a parallel multi-head self-attention module and a compact temporal convolutional block, enabling simultaneous capture of long-range process dynamics and fine-grained fault-onset localisation from raw sensor windows. TA-FaultNet is evaluated on the publicly available Skoltech Anomaly Benchmark under five operational classes and assessed through a comprehensive battery of experiments including baseline comparisons, systematic component ablation, cross-experiment generalisation, andprogressive noise-injection testing. The proposed architecture decisively outperforms eight competing methods spanning classical anomaly detectors, standalone recurrent and convolutional networks, and the Transformer, while remaining lightweight enough for edge gateway deployment. Attention weight visualisations expose fault-specific temporal activation patterns, providing maintenance engineers with interpretable diagnostic evidence beyond bare classification labels.

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Massila Kamalrudin mail -

Mustafa Musa mail

link https://doi.org/10.54216/IJWAC.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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International Journal of Wireless and Ad Hoc Communication | 2026

Event-Selective Fog Microbatching for Wireless Sensor IoT Devices: A Data-Driven Study Using Edge-IIoTset Features

Wireless sensor IoT devices increasingly operate under strict energy, latency, and security constraints while generating high-frequency telemetry that cannot be forwarded continuously to remote clouds. This paper presents an event-selective fog microbatching model for wireless sensor streams in which local novelty scoring, fog-side buffering, risk-preserving retention, and energy-aware scheduling are jointly optimized. Unlike conventional anomaly-detection pipelines, the proposed method treats communication reduction as a primary design objective and binds it mathematically to attack-evidence preservation. A reduced feature-level experimental file following the public Edge-IIoTset label structure and selected network/sensor attributes is used to evaluate traffic selectivity, uplink reduction, fog latency, energy saving, and detection performance. The model assigns each observation window a novelty score, suppresses redundant low-information traffic, and groups retained events into load-aware microbatches at the nearest fog node. The proposed model is extended with stochastic retention bounds, microbatch-delay stability, radio-energy equations, and risk-constrained threshold calibration. Experimental results show that the design reduces uplink load and radio-energy consumption while preserving strong attack discrimination across distributed wireless sensor traffic. The findings support a broader use of fog computing as a selective communication-control layer for dense, security-sensitive wireless sensor IoT deployments.

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Raden Aur Aachman Azakiyullah mail -

Aiswan Aumanti mail

link https://doi.org/10.54216/IJWAC.100201

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2026

Fog-Assisted Trust and Anomaly-Aware Scheduling for Wireless Sensor IoT Devices

Wireless sensor Internet of Things (IoT) devices increasingly generate time-sensitive traffic that cannot be efficiently inspected only in a remote cloud. Fog computing reduces the distance between sensing devices and decision logic, but fog nodes must jointly manage latency, queue pressure, wireless channel variability, energy use and security risk. This paper presents FogSense-TSA, a trust-aware and anomaly-aware scheduling model for wireless sensor IoT traffic in fog computing environments. The model integrates traffic intensity, wireless link behaviour, fog-resource state and temporal trust into a local decision process that determines whether a device-window should be accepted, quarantined at the fog layer or escalated to cloud inspection. The empirical analysis is conducted using a reduced analysis-ready file aligned with a recent public IoT device-identification and anomaly-detection setting. The proposed formulation introduces three algorithmic components: online trust-risk scheduling, load-aware fog placement and adaptive threshold calibration. Mathematical analysis is provided for evidence aggregation, trust stability, latency decomposition, energy cost, constrained placement and computational complexity. The results show that fog placement substantially reduces service latency relative to cloud-only routing while preserving high anomaly-discrimination capability. The strongest predictors are trust score, flow intensity, jitter, fog CPU load, payload entropy and queue pressure, indicating that fog-layer security should be coupled with wireless access and resource conditions rather than treated as a separate classifier. The study provides a reproducible and interpretable basis for designing lightweight security and scheduling modules for wireless sensor IoT deployments.

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Arash Salehpour mail -

Tamara Zhukabayeva mail

link https://doi.org/10.54216/IJWAC.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2026

Tiny Intelligence in Fog-Assisted Wireless Sensor IoT Networks: A Review of Deployment Patterns, Resource Trade-offs, and Open Challenges

Wireless sensor IoT networks are moving from simple measurement pipelines toward distributed systems where sensing, interpretation, filtering, and coordination are divided across devices, fog nodes, and cloud services. This review examines that transition through the lens of tiny intelligence, with special attention to how small models, local event filters, federated updates, service placement, and privacy controls reshape fog-assisted wireless sensor deployments. The paper does not treat fog computing as a generic latency layer. Instead, it studies fog as a governance and orchestration layer that decides which data should stay at the device, which events should be aggregated nearby, and which models require cloud-level supervision. A structured comparison of prior studies is provided across architecture, TinyML, federated learning, placement, security, benchmarking, and lifecycle coverage. The synthesis shows that the literature has matured in modelling fog resources and building lightweight inference functions, but remains fragmented in lifecycle management, cross-layer wireless awareness, privacy accounting, and reproducible evaluation. The review concludes with a research agenda for sensor-to-fog intelligence pipelines that are adaptive, auditable, energy-aware, and suitable for long-lived cyber-physical deployments.

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Aygul Z. Ibatova mail -

Baumuratova Dilaram mail

link https://doi.org/10.54216/IJWAC.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2026

A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks

Battery-powered sensor nodes expire when their energy reserves are depleted, terminating data collection regardless of the physical integrity of the hardware. Solar harvesting offers a viable path to perpetual operation, but only when the routing layer can continuously track the time-varying energy state of every node and steer traffic away from nodes likely to be power-starved in the near future. Classical clustering and chain-based protocols select forwarding paths without regard to harvested energy, leading to premature node death even when sufficient solar income would have been available to sustain operation. This paper presents a deep reinforcement learning framework in which each sensor node operates an independent Deep Q-Network agent that adapts its next-hop forwarding decision based on local battery state, short-horizon solar energy forecasts, link quality estimates, and the residual energy levels of candidate neighbours. A lightweight LSTM sub-model provides the solar prediction horizon that the agent uses as part of its state representation, enabling it to distinguish nodes that are temporarily depleted but will recover from those whose batteries are trending toward permanent failure. Extensive simulation across a 100-node deployment over 3,000 operational rounds confirms that the proposed approach substantially extends network lifetime, improves packet delivery, and reduces wasted harvested energy compared with five competitive baselines. Reward function ablation, scalability experiments, and an energy neutrality verification further validate the design choices and confirm stability across a wide range of deployment conditions.

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Suhasini Monga mail -

Damandeep Kaur mail

link https://doi.org/10.54216/IJWAC.100204

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2026

Frequency-Aware Antenna Configuration for Reliable Wi-Fi Communication Networks

Dense Wi-Fi deployments are often tuned by changing channel width or adding access points, while the joint effect of antenna gain, operating frequency, wall loss, and network interference receives less systematic attention. This paper presents a frequency-aware antenna configuration model for Wi-Fi communication networks operating in the 2.4, 5, and 6 GHz bands. The model combines an indoor link budget, antenna-pattern classes, bandwidth dependent noise, an airtime-overlap penalty, and a coverage-assurance score that balances signal quality, throughput, latency, and packet error. A reproducible design-space table is generated from a validated Wi-Fi engineering model and analyzed across five deployment scenarios, three antenna families, four channel widths, and multiple client distances. The results show that higher frequency bands improve short-range capacity but deteriorate faster under distance and wall loss, while directional antenna gain can recover a substantial part of the lost link margin. The paper provides planning rules for selecting antenna type, frequency band, and channel width according to coverage, capacity, and interference risk. The work is intended for Wi-Fi network designers who need interpretable engineering evidence rather than a black-box prediction model.

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Mohammed. I. Alghamdi mail -

Abdul Rahaman Wahab Sait mail

link https://doi.org/10.54216/IJWAC.100205

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2026

WS-STACK: A Weighted Stacking Ensemble with Multi-Criteria Feature Selection for Multi-Class Traffic Classification and Anomaly Detection in Heterogeneous Wireless Sensor Networks

Heterogeneous Internet-of-Things deployments expose wireless sensor networks to a diverse and continuously evolving threat landscape encompassing distributed denial-of-service flooding, network reconnaissance scanning, and brute-force credential attacks. Existing intrusion detection approaches predominantly adopt single-classifier architectures and binary labelling, which are ill-suited to the multi-class, class-imbalanced traffic characteristic of real-world IoT sensor deployments. This paper proposes WS-STACK, a Weighted Stacking ensemble that combines five heterogeneous base learners—Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbours, and Gradient Boosting—under an ℓ2-regularised Logistic Regression meta-learner trained on cross validationgenerated probability features. A three-stage feature engineering pipeline comprising mutual information filtering, variance inflation factor pruning, and correlation-based elimination reduces the 83 dimensional RT-IoT2022 feature space to 20 informative features, and the Synthetic Minority Over-Sampling Technique corrects the six-fold class imbalance prior to training. Evaluated on 83,000 labelled network flow records from the publicly available RTIoT2022 benchmark spanning four benign traffic patterns and seven attack categories, WS-STACK achieves 99.61% classification accuracy, a weighted F1-score of 0.9960, and an AUC-ROC of 0.9978, outperforming every individual base classifier and five recently published state-of-the-art baselines. The false positive rate is reduced to 0.0006, and ten-fold cross-validation confirms μacc = 0.9959 (σ = 0.0004). Ablation experiments identify SMOTE as the single most critical preprocessing component, and noise robustness tests confirm 98.81% accuracy under 20% Gaussian feature perturbation. The framework is grounded through a formal variance-reduction proof and a channel-energy anomaly model that establishes the physical motivation for packet-rate features as the dominant intrusion detection signal in constrained wireless sensor networks.

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Zainab Hussein Arif mail -

Nureize bt Arbaiy mail

link https://doi.org/10.54216/IJWAC.100206

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new