Communication Performance Evaluation Using Compression Processing for IoT Systems in Mobile Environments
Chisa Ito, Atsuko Takefusa, Hidemoto Nakada, Masato Oguchi · 2024
Various sensor data from Internet of Things (IoT) devices are expected to be routinely collected, analyzed, and utilized in the cloud. However, the required communication throughput and latency for various services must be maintained when collecting IoT data in mobile environments. IoT communication involves a large amount of small-scale streaming data, necessitating efficient transmission methods tailored to the communication environment. In this study, we investigate the effectiveness of compression processing by varying the publish/subscribe data characteristics and data compression ratios to improve performance. In experiments, the performance of MQTT communication over SINETStream is investigated, varying parameters such as data size, compression algorithm, data characteristics, and data compression ratio. The results show that performance is improved for highly compressible data, and the performance difference becomes more pronounced as data size increases. There is no correlation between compression time and data compression ratio, and the impact of compression time on overall execution time is slight, thereby confirming that selecting appropriate algorithms for each data characteristic and applying compression processing according to the data size effectively improves performance.