Workforce Sustainable Technology Leadership: A Cross-Country Measurement Model for Human Resources, Leadership, and Sustainable Business

Dina El; Dina K.

doi:https://doi.org/10.54216/JSDGT.060204

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.

groups

Aygul Z. Ibatova mail -

Baumuratova Dilaram mail

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

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

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

Details open_in_new

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.

groups

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.

groups

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

Journal of Sustainable Development and Green Technology | 2026

Workforce Sustainable Technology Leadership: A Cross-Country Measurement Model for Human Resources, Leadership, and Sustainable Business

Sustainable business transformation is usually described as a technology problem, a leadership problem, or a human-resource problem. In practice, it is all three. This paper develops a cross-country analytical model that links workforce capability, responsible digital leadership, and sustainable operating capacity into a single diagnostic framework. A curated public-indicator extract was prepared from World Bank World Development Indicators, Worldwide Governance Indicators, and labour-market indicator definitions, covering 41 economies with recent values for internet use, R&D intensity, advanced workforce capacity, female senior management, renewable-energy reliance, carbon pressure, governance effectiveness, and digital s kills. The proposed Workforce Sustainable Technology Leadership Index (WSTLI) is used to compare countries, identify capability gaps, cluster transformation profiles, and simulate the effect of combined upskilling, leadership governance, and green technology interventions. The analysis shows that digital readiness alone is insufficient; several economies with high connectivity still show weak sustainability operating capacity, while others with stronger renewable-energy profiles lack the digital-HR capability needed to convert sustainability into scalable business practice. The paper provides a practical measurement architecture for HR leaders, executives, and policy-oriented business analysts who need to align workforce strategy with technology-enabled sustainability.

groups

Dina El Horr mail -

Dina K. Hassan mail

link https://doi.org/10.54216/JSDGT.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new