Integrating Edge Computing and Social Computing for Smart Energy Optimization in Public Buildings: An Empirical Study Based on the GECA–CAFCLA Framework

The rapid proliferation of Internet of Things (IoT) devices in smart buildings has intensified data generation, creating critical challenges for cloud-centric architectures, including latency, bandwidth congestion, scalability limitations, and rising operational costs. This paper proposes a professionally engineered integration of the Global Edge Computing Architecture (GECA) within the CAFCLA framework to enhance smart energy efficiency in public buildings. The study combines architectural design, experimental deployment, and behavioral validation using the UTAUT model and Structural Equation Modeling (SEM). A two-phase experimental evaluation (non-edge vs. edge-based) conducted over eight weeks demonstrated a 56.71% reduction in cloud data traffic and estimated operational savings between 22–35%. Behavioral analysis confirmed significant adoption drivers (p < 0.05). The proposed framework improves scalability, reduces cloud dependency, enhances data integrity, and supports real-time energy optimization suitable for smart city ecosystems.