Emerging technologies such as Cloud Computing, 5G, the Internet of Things (IoT), and Edge Computing demand the management of large-scale and highly dynamic network infrastructures. Traditional network configuration does not scale efficiently, whereas Software-Defined Networking (SDN) enables centralized control and simplified management. Despite these benefits, SDN environments still face significant challenges related to security and fine-grained anomaly detection. Several studies have demonstrated the effectiveness of computational intelligence (CI) techniques for anomaly detection in SDN. However, the diversity of network anomalies and CI-based solutions introduces substantial heterogeneity, making model selection and integration challenging. This paper proposes a reference architecture designed to validate, promote, and explain the suitability of different CI techniques for distinct network anomaly scenarios. The proposed architecture adopts a hexagonal microservices design and a unified information model aligned with the application, information, and process layers of the TM Forum Open Digital Architecture (ODA). Validation was performed through a proofof-concept prototype using two datasets and seven machine learning algorithms. The results demonstrate the importance of architectural flexibility, enabling the dynamic integration and replacement of CI models to support adaptive and scalable SDN anomaly detection.
Rivaldo Fernandes, B. Dalmazo, A. Riker et al.· International Conference on...· 0 citations
This paper presents a low-cost IoT-based architecture for residential electricity metering and consumption forecasting, centered on a single-phase smart meter with Wi-Fi connectivity and an ESP32-S3 microcontroller. The proposed system combines local signal acquisition with an end-to-end communication infrastructure based on MQTT, enabling real-time transmission of electrical measurements from the edge device to a remote server for storage, visualization, and predictive analysis. Experimental results demonstrated satisfactory metering performance, with an average current MAE of 0.23 A and MAPE of 4.56% when compared with a CW500 reference power analyzer. From the telecommunications perspective, the communication tests showed low gateway latency, ranging from 1.53 to 12.8 ms, and server latency between 203.63 and 290.86 ms, indicating adequate responsiveness for real-time monitoring applications. For consumption forecasting, the AI models were trained and evaluated using the Low Carbon London dataset rather than data collected entirely by the prototype; the 1D CNN achieved MAE = 0.009109 and MSE = 0.000195, while the LSTM obtained MAE = 0.015597 and MSE = 0.000538. The architecture integrates open-source networking and data services, including Mosquitto, Telegraf, InfluxDB, Grafana, and Docker Compose, resulting in a replicable and scalable platform for smart energy monitoring in residential IoT environments.
E. L. de Sousa, L. A. de Aquino Marques, Israel da Silva Felix de Lima et al.· Annals of Telecommunications· 0 citations
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