An Internet of Things-Based Smart City Architecture Model for Optimizing Urban Energy Management

Abstract

This study proposes an Internet of Things (IoT) based smart city architecture to optimize urban energy management by integrating heterogeneous sensing, governed data harmonization, analytics-driven optimization, and dependable actuation in a closed-loop workflow. The research addresses a persistent urban challenge: energy-related data and controls are typically fragmented across utilities, municipal services, large buildings, and emerging mobility infrastructure, limiting citywide efficiency and peak-load mitigation. Guided by the theoretical lenses of IoT, Cyber-Physical Systems (CPS), and Demand Response (DR), the proposed architecture is structured into device/perception, interoperability and communication, semantic data management, analytics and optimization, and actuation/feedback layers, supported by cross-cutting security, privacy, and governance controls. The results indicate that the architecture strengthens cross-domain situational awareness, converts analytics into actionable and auditable decisions, and improves operational resilience through edge cloud partitioning and degraded-mode operation under imperfect data and connectivity. The study contributes a reference blueprint that links technical feasibility with institutional accountability, enabling scalable adoption and clearer evaluation of performance. Future work should emphasize longitudinal field validation, standardized metrics, and privacy-preserving optimization to enhance generalizability and readiness for deployment.