Data Compression in LoRa Networks: Performance and Energy Trade-Offs of Classical and Cutting-Edge Compression Algorithms

The growing number of Internet of Things (IoT) devices has driven the need for energy-efficient communication in long-range, low-power networks like LoRa. LoRa offers wide coverage with minimal transmission power. However, radio communication remains the main energy consumer in end devices. Data compression can mitigate this issue by reducing packet size and transmission frequency. This work presents a comprehensive evaluation of classical and cutting-edge lossless compression algorithms applied to LoRa networks. Evaluated algorithms include Huffman, LZW, BSC, CMIX, PAQ8PX, GMIX, and LSTM-compress. Experiments were conducted using a Raspberry Pi 5 integrated with an RFM95W LoRa module and INA219 sensors to measure real-time power consumption, CPU load, and memory usage. Results show that classical methods, particularly LZW, achieve the best energy efficiency and reduce LoRa transmission energy by up to 7.41%. In contrast, cutting-edge machine learning (ML)-based algorithms, such as CMIX and PAQ8PX, achieve higher compression ratios but exhibit excessive computational and memory overhead, resulting in negative energy gains. Metadata overheads, including dynamic Huffman tables (28-128 bytes), also affect payload efficiency for small packets. These findings indicate that LZW is the most practical choice for energy-constrained LoRa nodes. At the same time, modern compressors, including ML-based ones, are better suited for gateways or edge servers with higher computational capacity. An open-source implementation of the experimental framework and scripts used in this study is available in the project's public GitHub repository.