Optimization of LoRa for Distributed Environments Based on Machine Learning

In the rapidly evolving Internet of Things (IoT) field, wireless communication technologies have revolutionized industries by connecting smart devices for extensive data sharing and automation. However, distributed wireless communication systems often face limited signal coverage and high maintenance costs. This paper introduces optimization techniques based on machine learning in a distributed environment, aiming to design a low-power and long-range LoRa network for indoor and outdoor IoT applications. The orthogonal combinations of transmission parameters and the K-means approach effectively address the avalanche effects and maximize the throughput of its data collision avoidance algorithm (ALOHA protocol) for improving the network's overall performance.