# Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level
###### Abstract  
LoRaWAN networks rely heavily on the adaptive data rate algorithm to achieve good link reliability and to support the required density of end devices. However, to be effective the adaptive data rate algorithm needs to be tuned according to the level of mobility of each end device. For that purpose, different adaptive data rate algorithms have been developed for the different levels of mobility of end devices, e.g., for static or mobile end devices. In this paper, we describe and evaluate a new and effective method for determining the level of mobility of end devices based on machine learning techniques and specifically on the support vector machine supervised learning method. The proposed method does not rely on the location capability of LoRaWAN networks; instead, it relies only on data always available at the LoRaWAN network server. Moreover, the performance of this method in a real LoRaWAN network is assessed; the results give clear evidence of the effectiveness and reliability of the proposed machine learning approach. LPWAN; ADR; LoRaWAN +
Footnote †: journal: Sensors  
definition  
For mobile EDs, the network-based ADR strategy does not work because of the unpredictable channel attenuation which occurs as the ED moves. Rather, with mobile EDs, ADR is performed \"blindly\" by the end device. This is referred to as \"Blind ADR\" [7]. In addition to [7], some strategies to solve this problem rely on a sort of network slicing method that appears in the literature, such as in [8; 9], wherein the nodes' transmission's 40 parameters are tuned with respect to their mobility. The effectiveness of these approaches comes from their taking a proactive approach to the mobility of the EDs, in particular by monitoring the degree of mobility of an ED in which it is possible to update the transmission parameters to make possible a more reliable communication [9]. Other approaches appeared from specific vertical applications such as in [10], where cattle monitoring is considered, where \"ADR techniques to most efficiently find the optimal data rate for a firmware update\" can be found. The importance of ADR is highlighted in [11], where a \"study of environmental parameters impact in LoRaWAN\" is carried out, concluding that \"snowing leads to high fluctuations in SNR and RSS when Adaptive Data Rate (ADR) is disabled\". Finally, recently in [12], the authors extend their previous work [8] applying a variable-order hidden Markov model to predict the ED mobility pattern in the case of \"unknown or undefined trajectories\". Eventually, the recent paper [13] in Table 1 presents a survey of different ADR algorithms that appeared in the literature. It must be highlighted that it is possible to achieve a quite precise location of the ED in the LoRaWAN networks, just relying on the LoRa signal received from the gateways. For example, in [14] the authors report the results of two measurements' campaigns by using a machine learning approach and conclude that \"LoRaWAN-based localization with relatively dense gateways (GWs) deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers\". Furthermore, in [15] it is reported that the LoRa Cloud (tm), a tool from Semtech providing \"geolocation services based on TOA and/or RSSI observations\", has been made available worldwide. However, the location capability is still not widely implemented in LoRaWAN networks, due to its technical complexity and, more importantly, the cost related to its implementation, especially on a large scale. The main contribution of our paper is to propose a technique, based on machine learning, to classify the level of mobility of an ED relying purely on the data available on the NS, without the need to resort precise location techniques, based on the processing of physical signals, e.g., by making use of the time of arrival (ToA) used in [15]. Based on this classification, a tuning of the ADR can be made reliably, having, for example, different possible ADR algorithms for different levels of mobility.