Adaptive Data Compression Framework for Network Transmission Optimization Based on Entropy and Bandwidth Analysis

This paper addresses the problem of efficient data transmission under dynamically changing network and computational conditions by proposing an adaptive data compression method based on context-aware selection of compression algorithms and their parameters. Unlike conventional static approaches, the proposed method performs real-time analysis of data characteristics, network bandwidth, latency, and available computational resources, enabling dynamic selection of the optimal compression strategy through multi-criteria optimization. The scientific novelty of the work lies in the integration of data-driven and environment-aware adaptation within a unified decision-making framework that simultaneously minimizes end-to-end transmission delay while balancing compression ratio and processing overhead. Experimental evaluation was conducted using both synthetic datasets and the standard Silesia Corpus benchmark. The synthetic datasets included repetitive low-entropy text (repeated.txt), structured JSON data (structured.json), moderate-complexity text (example.txt), and high-entropy binary streams (random.bin), representing realistic web content and raw data transmission scenarios. The Silesia Corpus, containing approximately 200 MB of heterogeneous real-world files, including text, binaries, and images, was used for validation and benchmarking. The proposed method was evaluated using compression algorithms such as LZ4, Zstandard, Brotli, and ZSTD under different network conditions and system loads. Experimental results show that the adaptive approach reduces total transmission time by an average of 23%, improves compression efficiency by 16%, and decreases computational resource consumption by 13% compared to conventional static compression methods. The software implementation is based on a modular service-oriented architecture that supports real-time monitoring, dynamic algorithm switching, and scalable deployment in distributed, cloud, streaming, and Internet of Things environments.