Abstract
Nowadays, the LoRa technology is one of the promising technologies used for the Internet of Things (IoT) networks. Over the LoRa transmission link, two devices can communicate with each other over a long distance. As perspective research on LoRa mesh network, it is necessary to consider the link quality estimation (LQE) between neighbor nodes to choose reliable routes. In this paper, we propose a LQE method to classify the connection level between two nodes. The LQE method is developed based on the kernel support vector machine (kSVM), which is one of the machine learning techniques used in classification problems. Series of experiments were performed to collect a dataset consisting of received signal strength indicator (RSSI), signal-to-noise ratio (SNR) of received packets, and packet reception rate (PRR). The trained model shows a high prediction accuracy (mean = 95%) while using 10% of the dataset for training.
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The publication has been prepared with the support of the grant from the President of the Russian Federation for state support of leading scientific schools of the Russian Federation according to the research project SS-2604.2020.9.
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Pham, V.D., Hao Do, P., Le, D.T., Kirichek, R. (2021). LoRa Link Quality Estimation Based on Support Vector Machine. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2021. Lecture Notes in Computer Science(), vol 13144. Springer, Cham. https://doi.org/10.1007/978-3-030-92507-9_9
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