Lossy Data Compression for IoT Sensors: A Review

Internet of Things (IoT) can be considered a suitable platform for industrial applications, enabling large systems that connect a huge number of intelligent sensors and subsequent data collection for analytical applications. This factor is responsible for the substantial increase in the current volume of data generated by IoT devices. The large volume of data generated by IoT sensors can lead to unusual demands on cloud storage and data transmission bandwidths. A suitable approach to address these issues is through data compression approaches. This article presents a systematic review of the literature on lossy data compression algorithms that allows the systems to reduce the data detected by IoT devices. Lossy algorithms have a good compression ratio, preserving data quality and minimizing compression errors. A taxonomy was proposed from the review results, and the main works were classified, analyzed, and discussed.

Automated summary

This article presents a systematic review of literature on lossy data compression algorithms for reducing the data detected by IoT devices. Lossy algorithms have good compression ratio, preserve data quality, and minimize compression errors. A taxonomy was proposed based on the review results.