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The advent of the Internet of Things (IoT) has revolutionized numerous sectors, including water resource management. This paper presents the design and implementation of an IoT-based Dam Instrumentation System aimed at enhancing the monitoring, safety, and operational efficiency of dams. The proposed system integrates various sensors (such as pore pressure, inclination, stress, strain, water level, pressure, temperature, and seismic activity) with wireless communication networks to provide real-time data acquisition and analysis. Through a cloud-based platform, the system facilitates remote monitoring, predictive maintenance, and data-driven decision-making, significantly improving the dam’s resilience against structural failures and environmental threats. By employing machine learning algorithms for anomaly detection and predictive analytics, this system enables proactive risk management, thereby enhancing safety protocols. Such smart systems can play a critical role in improving dam safety and resource management in the face of increasing climate variability and aging infrastructure.

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IoT-Based Monitoring of the Health of Dam

  • 会议论文

  • 发表日期：

    2026年

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  • Ambarnath Banerji,
  • Abir Das,
  • Sabyasachi Nandy,
  • Sufal Dhar,
  • Tiyasha Sarkar,
  • Ankur Mondal,
  • Souvik Das,
  • Soumyadeep Chatterjee,
  • Pallav Dutta,
  • Bansari Deb Majumder,
  • Subimal Roy Barman

摘要

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, including water resource management. This paper presents the design and implementation of an IoT-based Dam Instrumentation System aimed at enhancing the monitoring, safety, and operational efficiency of dams. The proposed system integrates various sensors (such as pore pressure, inclination, stress, strain, water level, pressure, temperature, and seismic activity) with wireless communication networks to provide real-time data acquisition and analysis. Through a cloud-based platform, the system facilitates remote monitoring, predictive maintenance, and data-driven decision-making, significantly improving the dam’s resilience against structural failures and environmental threats. By employing machine learning algorithms for anomaly detection and predictive analytics, this system enables proactive risk management, thereby enhancing safety protocols. Such smart systems can play a critical role in improving dam safety and resource management in the face of increasing climate variability and aging infrastructure.