01739nas a2200133   4500008004100000245008000041210006900121520118000190100002101370700002201391700002601413700002701439856013901466       2021                        eng d00aSpreading Factor Analysis for LoRa networks: A supervised learning approach0 aSpreading Factor Analysis for LoRa networks A supervised learnin3 aToday, the Internet of Things (IoT) has been introduced in our lives, giving a variety of solutions and applications. The critical requirements for de-vices connected to the IoT are long battery life, long coverage, and low de-ployment cost. Some applications require the transmission of data over long distances, thus Low Power Wide Area Networks (LPWAN) have emerged, with LoRa being one of the most popular players of the market. In order, to improve energy consumption and connectivity problems, machine learning can be used in LoRa networks. In this paper, we intend to improve the energy consumption of end nodes by using machine learning models. For this reason, we present a comparison of classification algorithms, specifically, the k-NN, the Naïve Bayes, and Support Vector Machines (SVM), for the Spreading Factor (SF) as-signment in LoRa networks. The simulation results indicate that, both energy efficiency and reliability in IoT communications could be significantly im-proved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.1 aBouras, Christos1 aGkamas, Apostolos1 aKatsampiris, Spyridon1 aPapachristos, Nikolaos  uhttps://telematics.upatras.gr/telematics/publications/spreading-factor-analysis-lora-networks-supervised-learning-approach?language=en