NEURO-SYMBOLIC NETWORK AUTOMATION: COMBINING SYMBOLIC REASONING AND DEEP LEARNING FOR NEXT-GEN NETWORKS

Abstract

Smart cities are reshaping cityscapes via improved operational efficiency, resource management, and sustainability made possible by the convergence of IoT and AI. Transportation, energy management, environmental monitoring, public safety, and healthcare are just a few of the vital urban areas that might benefit from AI-processed data acquired in real-time via distributed sensor networks made possible by the Internet of Things (IoT). Important obstacles, including as data heterogeneity, security holes, computational limitations, and regulatory compliance, pose serious threats to the potential benefits of this convergence. Edge computing, federated learning, and privacy-preserving AI models are some of the important enabling technologies examined in this article, which offers a thorough overview of the possibilities offered by IoT-AI integration. Advanced mitigation strategies such as blockchain-enhanced security, decentralised intelligence, and adaptive AI-driven urban systems are explored in the study, along with major challenges such as interoperability constraints, security risks, and ethical considerations. 5G, digital twins, and quantum computing will play a revolutionary role in next-generation smart cities, and this article also explores their potential future roles. This study provides important insights for academics, politicians, and urban planners who are working to create smart city ecosystems that are resilient, scalable, and sustainable by combining current advances and filling significant research gaps.