AI, are we considering to implement it in our data loggers ?

AI data logger

YDOC is continuously considering new features, including AI, to enhance the functionality of their data loggers.

The historic data collected by data loggers is an ideal feed for AI to process and analyze for various purposes, such as:

  • Predictive maintenance: AI can help detect anomalies and identify potential failures before they occur, reducing downtime and costs.
  • Forecasts: It can use historical data to generate accurate and reliable forecasts for demand, supply, weather, and more.
  • Early warnings: AI can alert users to critical situations, such as environmental hazards, security breaches, or equipment malfunctions
  • Data filtering: it can help filter out noise and irrelevant data, improving the quality and efficiency of data analysis

However, implementing AI features on the data loggers themselves may not be the best option, as it can increase the hardware costs and battery drain, which would negate the benefits of why affordable low power remote data loggers are in use. Instead, it may be better to implement such features at the cloud system side, as it can correlate the data with many other sources to come to even better insights. This way, the data loggers can focus on their core function of collecting and transmitting data, while the cloud system can handle computational intensive processing and analysis.