Adaptive Entropy Prediction-based Lossless Compression for Efficient Data Transmission in Terrestrial and Underwater IoT Systems

Abstract

Demand for quick lossless data compression systems has become more important recently due to the growing deployment of Internet of Things (IoT) devices in terrestrial and underwater environments, under restricted bandwidth, latency and energy consumption conditions. Current dictionary-based and entropy-driven compression methods depend on reactive adaptation and fixed block processing, which makes them less useful in situations where sensing is constantly changing. This paper presents a self-optimizing entropy prediction-assisted lossless compression framework (EP-SLZW), where the compression technique is based on the expected data redundancy and current channel condition. To achieve optimal results, lightweight entropy prediction is employed in combination with adaptive block segmentation and dual dictionary learning between processing and compression. The optimization component of the proposed method enhances the compression strategy by taking care of all important environmental factors to provide the best solutions for radio frequency and acoustic communication channels. The method is evaluated with the help of the MQTT and CoAP protocols through NetSim simulations and hardware experiments. The results show that the proposed approach performs better in comparison to conventional LZW as well as Huffman and run-length encoding techniques, providing better throughput, compression ratio, and less end-to-end delay when dealing with resource-constrained IoT devices.