AI-Powered Smart Connect Ecosystems with BERT, NLP, WSN, and SDN for Sustainable IT Infrastructure Modernization

Abstract

Smart Connect Ecosystems—networks of interconnected devices, human users, data flows, and policy or regulatory frameworks—are increasingly important for modern IT infrastructure that is sustainable, resilient, and responsive. This paper proposes a framework that integrates Natural Language Processing (NLP) with transformer‑based models such as BERT (Bidirectional Encoder Representations from Transformers) to enable intelligent connectivity, policy compliance, and sustainability assessment across IT systems. The framework supports unstructured textual data (e.g. policy documents, environmental regulation, user feedback), structured data from sensors/IoT, and governance/social frameworks, combining them to better monitor, predict, and enforce sustainable behaviors, compliance, and system optimization. First, we outline motivation: IT infrastructures are under pressure—from energy consumption, regulatory demands, user expectations, and rapid technological changes—to evolve toward more sustainable models. Then we survey the existing literature on BERT & NLP applications in smart cities, sustainable IT, policy analysis, infrastructure planning, and industrial ecosystems. From this, gaps emerge in aligning policy, sustainability metrics, and real‑time operations via NLP. Our proposed framework has multiple layers: Data ingestion (IoT + documents), NLP policy/regulation parsing, semantic alignment & knowledge graphs, predictive models for sustainability/performance, feedback loops, and governance modules. We also describe potential evaluation methods. The framework offers several advantages, including enhanced compliance, data-driven decision-making, improved responsiveness, greater stakeholder transparency, and the potential to reduce energy consumption and carbon footprint. However, it also presents challenges, such as increased system complexity, significant computational and data resource requirements, privacy concerns, and ambiguities in policy and regulatory interpretation. In a hypothetical implementation within a smart city or industrial park, the framework demonstrates its ability to identify misalignments between operational activities and policy objectives, anticipate sustainability-related failures, and optimize overall performance.