Comparison of Statistical and Machine Learning-Based Approaches for Telemetry Data Size Reduction

The implementation of next generation beyond 5G use cases such as the Digital Twin (DT) requires the transportation of increasingly large amounts of sensor telemetry data. The reduction of the size of these time series shaped data through compression has many benefits, ranging from energy savings in systems where sensors have limited power to transport network bandwidth reduction. Both statistical analysis-based and machine learning (ML)-based approaches have been employed to tackle this task with varying degrees of success. In this paper, we compare two methods for time series data compression: a statistical similarity-based one, and an autoencoder (AE)-based one.