Multi-Task Drift-Oriented Deployment Update Mechanism for Meteorological Sensor Networks

The monitoring tasks of a meteorological sensor network will vary over time and environmental changes. To ensure the accuracy and reliability of the monitoring data, it is crucial to promptly detect the drift of the monitoring tasks, and then update the deployment of sensor networks accordingly. Due to the complexity of the meteorological sensor network and intricate relationships between meteorological indicators, the influence of multiple meteorological indicators needs to be considered comprehensively when detecting the drift of monitoring tasks. Therefore, this paper introduces a multi-task drift-oriented deployment update mechanism for meteorological sensor networks. This mechanism uses a single meteorological indicator to learn the trend of multiple meteorological indicators, It then constructs a graph topology of the sensor network, enabling the detection of multi-task drift and determining the necessity for network updates and redeployment. The effectiveness of this algorithm was evaluated using data from China's Ground-Based Basic Meteorological Observations Sensor Network. The experimental results demonstrate that the algorithm can effectively detect multi-task drift and judge the optimal update time of the sensor network deployment.