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In high-stability environments like server rooms, continuous monitoring is essential to promptly detect and report even minor environmental changes. Anticipating fluctuations and triggering timely alerts ensures proactive interventions to maintain optimal conditions. In this paper, we propose a system that employs minimal hardware, specifically an ESP SOC Devkit V1, to monitor the environmental quality of the network server room at HUFLIT University, Ho Chi Minh City. To detect potential environmental issues in multivariate time series data, we integrate a Variational Auto Encoder Bidirectional Long Short-Term Memory (VAE-Bi-LSTM) hybrid model as an unsupervised anomaly detection approach. As a result, our detection algorithm is capable of identifying issues that may not be immediately apparent, ensuring comprehensive monitoring. The system has been experimentally validated in real-world conditions, demonstrating not only stability and real-time interaction but also reliable long-term predictive accuracy, confirming its effectiveness in practical applications.

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Real-Time Monitoring and Anomaly Detection in Server Lab Environments Using ESP32 DevKit and VAE-Bi-LSTM Hybrid Model: A Case Study at HUFLIT University, Ho Chi Minh City

  • 会议论文

  • 发表日期：

    2025年

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  • Trong Le,
  • Long Le,
  • Nhan Tran,
  • Tho Nguyen,
  • Binh Nguyen,
  • Trung Nguyen,
  • Thien Pham,
  • Tho Quan

摘要

In high-stability environments like server rooms, continuous monitoring is essential to promptly detect and report even minor environmental changes. Anticipating fluctuations and triggering timely alerts ensures proactive interventions to maintain optimal conditions. In this paper, we propose a system that employs minimal hardware, specifically an ESP SOC Devkit V1, to monitor the environmental quality of the network server room at HUFLIT University, Ho Chi Minh City. To detect potential environmental issues in multivariate time series data, we integrate a Variational Auto Encoder Bidirectional Long Short-Term Memory (VAE-Bi-LSTM) hybrid model as an unsupervised anomaly detection approach. As a result, our detection algorithm is capable of identifying issues that may not be immediately apparent, ensuring comprehensive monitoring. The system has been experimentally validated in real-world conditions, demonstrating not only stability and real-time interaction but also reliable long-term predictive accuracy, confirming its effectiveness in practical applications.