arXiv:2610.35873v1 Announce Type: new Abstract: The integration of the Internet of Things (IoT) into monitoring systems has become increasingly significant across various fields, including energy management, environmental monitoring, and industrial applications. This paper proposes a smart, real-time, energy-aware monitoring system architecture for IoT using a brokerless Data Distribution Service (DDS) middleware. Environmental and electrical metrics are transmitted with high reliability, achieving an average latency of 1.76 milliseconds and a throughput of 57.1 Mbps. These metrics are processed by a multi-head Long Short-Term Memory network. Evaluated on the empirical TDC2.0 data center dataset, the architecture simultaneously performs three tasks: predicting thermal stress, detecting cooling inefficiency, and forecasting multi-step cooling power demand. The multi task network achieved an F1-score of 0.92 for proactive thermal stress prediction and demonstrated the capability to identify systemic cooling anomalies. Furthermore, the model generated consistent power forecasts across 5-minute, 10-minute, and 30 minute horizons, maintaining an R2 > 0.83. A post-hoc SHAP analysis indicated that the predictive logic aligns with established thermodynamic relationships. Overall, the proposed architecture offers a transparent and low-latency framework for critical facility management.