Data Compression in LoRa Networks: A Compromise between Performance and Energy Consumption

Abstract

The Internet of Things (IoT) end devices have major limitations related to hardware and energy autonomy. Generally, the highest energy consumption is related to communication, which accounts for up to 60% of consumption depending on the application. Among the strategies to optimize the energy consumed by communication, data compression methods are one of the most promising. However, most data compression algorithms are designed for personal computers and need to be adapted to the IoT context. This study aims to adapt classical algorithms, such as LZ77, LZ78, LZW, Huffman, and Arithmetic coding, and to analyse their performance and energy metrics in IoT end devices. The evaluation is performed in a device with an ESP32 processor and LoRa modulation. The study makes use of real datasets derived from two IoT applications. The results show compression rates close to 70%, a three-fold increase in the number of messages sent, and a reduction in energy consumption of 22%. An analytical model was also developed to estimate the gain in the battery life of the device using the adapted algorithms.