OTA Update Network Delay Modeling and Adaptive Compression Transmission Optimization

Over-the-air (OTA) updates often face unstable delay and limited bandwidth, which lower data transfer speed and reliability. This study built an adaptive OTA transmission method that combines a Bayesian delay prediction model with Brotli–LZMA compression. The model estimates short-term delay changes and adjusts compression level according to network conditions. Tests were done under simulated satellite and IoT links with bandwidth between 0.5 and 10 Mbps. The results showed that packet loss dropped by 41%, transfer rate increased by 29%, and compression time accounted for 3.8% of the total process. The prediction model reached a root mean square error (RMSE) of 18 ms, showing good accuracy in delay estimation. These results show that combining delay prediction with adaptive compression can make OTA transmission faster and more stable in low-bandwidth networks. The method can be used in satellite, IoT, and remote monitoring systems that require reliable OTA data delivery.