A lightweight Machine-learning based Wireless Link Estimation for IoT devices

Robust wireless communication between devices is crucial to ensuring the reliability of IoT systems. However, it is strictly relied on estimating the link quality of these devices, which is usually interfered with by environmental factors. In such a scenario, intelligent algorithms based on machine learning to select resistant communication links are promising solutions, but they demand sophisticated computation, limiting their deployment on resource-constrained IoT devices. Therefore, this paper introduces a lightweight link quality estimation algorithm, namely LLQE, built from the gradient boosting decision tree. The superiority of our proposal not only precisely assesses several levels of link quality but also is lightweight enough for resource-constraint devices. The evaluation results on publicly available datasets show that LLQE accurately estimates various link quality indicators with 97% accuracy.