Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues

Abstract

The rapid urbanization of modern cities has been propelled by the convergence of the Internet of Things (IoT) and Big Data, enabling real-time monitoring, predictive analytics, and intelligent automation across transportation, energy, healthcare, and public safety. This survey systematically reviews advancements in IoT-enabled infrastructure, Big Data analytics, edge and cloud integration, digital twin technology, and blockchain for secure and scalable data management. The literature selection focused on peer-reviewed works published from 2020 onward, prioritizing journal and conference papers that present concrete smart city deployment or technical frameworks. Each technological domain is examined with respect to its operational benefits, limitations, and role in enabling resilient urban ecosystems. Emerging trends such as 6G-enabled IoT, federated learning (FL), quantum computing, and swarm intelligence are explicitly contextualized in terms of their maturity, current research focus, and prospective impacts. To address persisting challenges, including scalability bottlenecks, interoperability gaps, cybersecurity threats, and data governance issues, the survey identifies actionable directions such as geospatial data standardization, energy-aware orchestration, and lightweight consensus mechanisms. These targeted measures provide a foundation for guiding future research and practice toward secure, efficient, and human-centric smart city development.