Smart Spreading Factor Assignment for LoRaWANs Low power wide area network (LPWAN) technologies offer affordable connectivity to massive number of lowpower devices distributed over large geographical areas. Focus of this work is one of the most promising LPWAN technologies: LoRa. LoRa offers long range communication and strong resilience to interference by proprietary modulation technique based on Chirp Spread Spectrum (CSS). LoRa modulation trades data rate for sensitivity and communication range by spreading symbols within a fixed channel bandwidth. Collisions in LoRaWAN networks are strongly related with spreading factor (SF) assignment of nodes which indeed effects network performance. In this work, a simulation environment to evaluate the performance of SF assignment schemes is implemented. Furthermore, a novel smart SF assignment strategy which utilizes Support Vector Machine (SVM) and Decision Tree Classifier (DTC) machine learning techniques for optimization of SF assignment is proposed. It is observed and presented that the proposed smart SF assignment techniques give promising simulation results in terms of packet delivery ratio (PDR).

Fast and Reliable LoRabased Data Transmissions LoRaWAN is a recently proposed MAC layer protocol which manages communications between LoRabased gateways and enddevices. It has attracted much scientific attention due its physical layer characteristics, but mainly due to its versatile configuration parameters. However, it is known that LoRaWANbased transmissions suffer from extensive collisions due to the unregulated access to the medium. For this reason, various techniques that alleviate the burst of collisions have been proposed in the literature. In this paper, we deal with the problem of fast data delivery in LoRabased networks. We model a network where transmissions follow a Poisson process. We compute the average packet success probability per Spreading Factor (SF) assuming orthogonal transmissions. We, then, formulate an SF optimization problem to maximize the success probability given an amount of data per node and a maximum data collection time window. We show xe2x80x93 both theoretically and using simulations xe2x80x93 that the overall success probability can be improved by approximately 100% using optimal SF assignments. We validate our findings using a 10node testbed and extensive experiments. Despite that experiments reveal the existence of interSF interference, our solution still provides the best performance compared to other LoRaWAN configurations.