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 emergence of 6G networks has revolutionized IoT environments with enhanced bandwidth, ultra-low latency, and ubiquitous connectivity. However, these complex and data-intensive networks demand intelligent, real-time anomaly detection mechanisms. Traditional methods fall short in dynamic settings, necessitating a fusion of cognitive computing and data analytics to ensure reliability, resilience, and security.This study aims to develop an integrated framework that combines cognitive computing and advanced data analytics to facilitate real-time anomaly detection in 6G IoT ecosystems. Additionally, it explores how environmental artists can contribute interpretive and ethical insights into algorithmic design through creative data representation. A hybrid approach is employed, integrating edge-based data collection, real-time processing with machine learning models, and cognitive systems for contextual anomaly detection. Environmental data such as air quality, temperature, and motion is collected via IoT sensors and analyzed using Python-based tools (Pandas, NumPy, TensorFlow, and Scikit-learn). Generative models, including Autoencoders and LSTM-based architectures, are implemented for detecting anomalies. Parallel to this, qualitative insights are gathered from environmental artists to explore aesthetic and ethical perspectives in system feedback. The Python-powered framework demonstrated high detection accuracy (F1-score > 0.93) with minimal false positives in simulated 6G environments. Integration with environmental art projects allowed real-time anomalies to be visualized through interactive installations, enhancing public awareness and interpretability. The use of Explainable AI (XAI) further improved transparency and trust in system outputs.Findings reveal that the use of Python not only accelerated model prototyping and deployment but also facilitated scalable integration across multiple edge nodes. Environmental artists provided a unique lens through which anomaly patterns were recontextualized, offering both ethical critique and public engagement. The convergence of art and AI opened up interdisciplinary pathways for inclusive, resilient system design. Integrating cognitive computing with Python-driven data analytics in 6G IoT systems significant improves real-time anomaly detection. Coupling these technologies with the interpretive frameworks of environmental artists enriches system transparency, ethical design, and societal impact. This multidisciplinary approach offers a new paradigm for intelligent infrastructure that is both technically robust and socially meaningful.