Abstract
Long Range Wireless Access Network (LoRaWAN) emerged as one of the promising Low Power Wide Area Networks (LPWAN) for IoT applications. It allows end-devices to reach a gateway and then the core network with a star topology in a wide area. Long Range (LoRa) transceivers send data packets according to a configuration or a set of parameter's values: Spreading Factor (SF), Payload size (PS), Bandwidth (BW) and Coding Rate (CR). These parameters must be fixed or adapted to application's requirements. Adaptive Data Rate (ADR) control system of LoRaWAN has been proposed to adapt modulation parameters dynamically based on the recent received packets. However, ADR control system doesn't adjust parameters considering the evolution of applications' Quality of Service (QoS) requirements. In this paper, we propose to cluster a set of LoRa transmission settings based on the measured QoS metrics such as Bit Error Rate (BER), Time on Air (ToA) and Received Signal Strength Indication (RSSI). We consider the set of settings' vectors as a cloud of points in a vector space while measured metrics are points' coordinates. Our method aims to map a set of LoRa transmission settings that offers the same QoS to the same cluster. We generate a set of transmission settings randomly and apply the Fuzzy C-Means (FCM) clustering algorithm on the resulting QoS metrics, Results show that the FCM clustering algorithm attribute membership values that best fit application requirements. This result could be used by LoRaWAN network servers to map each LoRa transmission setting to the application running on end devices.