Integrating Data Analytics and Cognitive Computing for Real-Time Anomaly Detection in 6G IoT Environments: Insights from Environmental Artists on Leveraging Advanced Algorithms in Art and Design

Abstract

The study has examined the intersection of data analytics and cognitive computing to identify anomalies in real-time in 6G-enabled IoT systems, in a distinct interdisciplinary approach through environmental art and design. The paper explored how 6G networks can be used to detect anomalies faster in dynamic environments, including smart-city systems, climate monitoring, and sustainable installations, by providing edge-intelligent IoT architectures coupled with high frequencies. The framework is improved through the incorporation of machine learning, deep neural networks, and adaptive analytics, which increase accuracy, latency, and decision. Contextual understanding is assisted by cognitive computing and enables systems to learn irregular patterns by using self-feedback loops. This study also includes the comments of environmental artists who apply algorithmic modeling and sensor-guided data to create adaptive interactive arts. Their innovative methods show that aesthetic visualization and algorithmic interpretation may guide real-time system responsiveness and anomaly mapping. It is a cross-disciplinary approach that connects technical efficiency to human-centered design thinking to allow the use of sustainable, visually communicative, and interpretative models to visualize anomalies. The results emphasize the use of cognitive data systems to transform intricate inputs in the environment to artistic outputs that are more understandable and interpretable. The paper found that a combination of artistic cognition and sophisticated computational intelligence would greatly enhance the interpretive richness, transparency and design-driven relevance of anomaly detection in future 6G IoT networks.