AI- and Cloud-Enabled Real-Time Online Smart BMS with SDN-Driven Data Management

Abstract

The rapid evolution of smart infrastructures has increased the demand for intelligent, connected, and energy-efficient building management systems (BMS). This paper proposes an AI- and Cloud-Enabled Real-Time Online Smart BMS architecture that leverages Software-Defined Networking (SDN) for optimized and secure data management. The framework integrates advanced AI-driven analytics and machine learning models with cloud computing to provide real-time monitoring, predictive maintenance, and adaptive control of building operations, including energy consumption, environmental conditions, and security systems. SDN plays a critical role in enabling dynamic, flexible, and secure network management across distributed BMS nodes, allowing seamless communication, bandwidth optimization, and rapid reconfiguration of network resources. The AI component processes data collected from IoT-enabled sensors and devices to identify patterns, detect anomalies, and predict system failures, enabling proactive maintenance and reducing operational downtime. The integration of cloud computing ensures scalability, high availability, and centralized data orchestration, supporting multiple smart buildings or campuses in a unified framework. The proposed system emphasizes online connectivity and automated decision-making, enabling zero-latency responses and intelligent control actions without human intervention. Furthermore, the architecture promotes energy efficiency, operational reliability, and occupant comfort, while ensuring resilience, security, and sustainability in modern building ecosystems. Case studies and simulations demonstrate the framework’s capability to optimize resource utilization, improve fault tolerance, and provide actionable insights for building operators and facility managers.