Author

Darlan Noetzold

h-index 3 View on Semantic Scholar

We have 2 of 18 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

A Low-Cost IoT Architecture for Micro-Zone Climate Prediction and Meteorological Forecasting

This article presents the design and deployment of ClimaBogotá v1.2, a climate prediction system tailored for high-altitude urban micro-zones in Bogotá, Colombia. The system combines low-cost IoT sensing, machine learning modeling, and cloud-based orchestration to enable scalable and affordable meteorological forecasting. Its architecture comprises Raspberry Pi-based weather stations, a Random Forest model trained on engineered temporal features, and an n8n-driven automation pipeline for real-time inference and dissemination via Telegram, PostgreSQL, and Grafana. With a Mean Absolute Error of 2.59 °C and an R2 of 0.6286 on a 30 min forecast horizon, the system demonstrates both predictive reliability and operational feasibility using free-tier cloud resources. Unlike traditional weather systems, ClimaBogotá emphasizes modularity, adaptability, and cost-efficiency, offering a replicable framework for decentralized climate monitoring in data-scarce urban environments. Temporal misalignment between sensor nodes was identified as the primary constraint, informing future enhancements toward distributed learning strategies.

César A. G. Mateus, Darlan Noetzold, Juan M. B. Skolik et al. · Electronics · 0 citations

Conference Jul 2026

Adaptive Quantum-Classical Cryptographic Selection: An RL-Based Architecture for DN25

We present Q-OPSEC, an adaptive middleware that uses supervised, unsupervised and reinforcement learning to select cryptographic strategies from classical, post-quantum and quantum-assisted (QKD) options. The selection is modeled as a multi-objective MDP that balances security, latency, computational and energy cost, and compliance. A negotiator and registry enforce hard constraints, handle endpoint compatibility and fallbacks, and store empirical cost profiles. Experiments in simulated smart environments and hardware-in-the-loop tests show high success rates ($>95 \%$) and context-aware adaptation. Limitations include reliance on simulated QKD channels, limited device profiling, empirically tuned hyperparameters, and evaluation in high-performance environments, which may not reflect IoT constraints; future work targets real QKD integration, broader benchmarking, robust RL methods, federated learning and explainability.

Darlan Noetzold, J. L. V. Barbosa, Juan F. de Paz et al. · International Conference on... · 0 citations