Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks.

Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based task model to generate the final output. Due to limited communication bandwidth, it is important for the compressor to learn only the features that are relevant to the task. Additionally, the final performance depends heavily on the total available bandwidth. In practice, it is common to encounter varying availability in bandwidth.