Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving $R^2$ values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness.

Efficient antenna orientation of transmitters is essential for improving wireless signal quality and coverage, especially in large-scale and complex 6G networks. Identifying the best antenna angles is difficult due to the nonlinear interaction among orientation, signal propagation, and interference. This paper introduces a digital twin-based evaluation approach utilizing ray tracing simulations to assess the influence of antenna orientation on critical performance metrics: path gain, received signal strength (RSS), and signal-to-interference-plus-noise ratio (SINR). A thorough array of orientation scenarios was simulated to produce a dataset reflecting varied coverage conditions. The dataset was utilized to investigate antenna configurations that produced the optimal and suboptimal performance for each parameter. Additionally, three machine learning models—k-nearest neighbors (KNN), multi-layer perceptron (MLP), and XGBoost—were developed to forecast ideal configurations. XGBoost had superior prediction accuracy compared to the other models, as evidenced by regression outcomes and cumulative distribution function (CDF) analyses. The proposed workflow demonstrates that learning-based predictors can uncover orientation refinements that conventional grid sweeps overlook, enabling agile, interference-aware optimization. Key contributions include an end-to-end digital twin methodology for rapid what-if analysis and a systematic comparison of lightweight machine learning predictors for antenna orientation. This comprehensive method provides a pragmatic and scalable solution for the data-driven optimization of wireless systems in real-world settings.

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Reconfigurable intelligent surfaces (RIS) have emerged as a promising enabler for beyond-5G and 6G networks, offering controllable propagation environments to enhance coverage and spectral efficiency. This study investigates and compares multiple RIS configuration strategies, including analytical baselines such as the phase gradient reflector (PGR) and focusing lens (FL), optimization-driven approaches via gradient-based optimization (GBO), and learning-assisted designs through hybrid Mixture-of-Experts (MoE) and CNN-based gating. A unified simulation framework was developed to evaluate amplitude and phase profiles, expert-selection heatmaps, and coverage improvement maps, alongside a detailed analysis of the average path gain evolution over iterations. Quantitative results show that PGR and FL achieve average path gains of −112 dB and −97 dB, respectively, while GBO attains the highest gain of approximately −92 dB. The Hybrid MoE achieves −93.5 dB with localized coverage enhancements exceeding 40 dB, whereas CNN-gating maintains smoother and more generalized coverage improvements up to 20 dB. Results demonstrate that while PGR and FL provide predictable yet limited performance, GBO yields the highest path gain at the cost of computational complexity. MoE balances interpretability and adaptability through smoother expert-weight distributions, whereas CNN-gating enforces sharper, binary-like spatial decisions, enhancing coverage in challenging blind spots. The comparative findings highlight a performance spectrum ranging from interpretable analytical models to highly adaptive learning-based schemes, revealing trade-offs between flexibility, computational cost, and generalization capability, while also underlining RIS’s potential for sustainable and energy-efficient networking. These insights position hybrid and learning-driven RIS designs as promising candidates for scalable, adaptive deployment in future wireless systems.

Accurate modeling of wireless signal propagation in complex environments is essential for the efficient planning of next-generation communication networks. This study investigates the impact of transmitter placement and elevation on signal coverage and interference levels within a structured campus environment. Using Sionna ray tracing, a differentiable and GPU-accelerated ray tracing framework, realistic 3D models of the Aydin Adnan Menderes University main campus were constructed and simulated. Three transmitter deployment scenarios—distributed, centralized, and optimized—were evaluated in terms of path gain, Received Signal Strength (RSS), and Signal-to-Interference-plus-Noise Ratio (SINR). The simulation results reveal how different spatial configurations influence signal propagation, coverage uniformity, and interference. The findings provide valuable insights into strategic transmitter placement for enhanced network performance in real-world deployments.