# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
###### Abstract  
A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio. LoRaWAN; adaptive data rate; mobility; Internet of Things; resource assignment; convergence period 2  
technologies (e.g., Cellular (3G/4G), Ethernet) to a centralized network server (NS) responsible for functions such as traffic management [7]. The NS is deployed in a cloud computing environment (rarely co-located near the GW [8]), accountable for collecting data from the GW and sending them to appropriate applications. Therefore, in the case of delay-sensitive applications, edge and fog computing solutions have also been studied in order to deploy LoRaWAN network components [9; 10], bringing services, such as advanced analytics and distributed storage, closer to the EDs [11]. In class \(A\), every UL transmission is followed by two receive windows, namely RX1 and RX2, for receiving a downlink (DL) acknowledgment (ACK) from the NS, as highlighted in Figure 1b. RX1 uses the same SF and channel set as the UL transmission, whereas the RX2 uses a dedicated channel (i.e., 869.525 MHz) and a fixed SF (i.e., SF12) [12]. If an ACK is not received in both, receive windows, a random delay, i.e., \(ACK\_TIMEOUT\), is added before a retransmission is conducted in order to avoid consecutive collisions. To this end, an ED chooses a random time between 1 and 3 s and applies a retransmission [13]. Figure 1: A brief working of long-range wide area network (LoRaWAN): **(a)** underlying architecture comprised of end devices (EDs), gateway (GW), network server (NS), and application server, and **(b)** transmission procedure in confirmed mode. Moreover, the class \(A\) EDs of LoRaWAN employ an adaptive data rate (ADR) mechanism at both the ED and NS [14]. The ED-managed ADR is only responsible for incrementing the SF. At the same time, NS-managed ADR adjusts both the SF and transmit power (TP) after receiving \(M\) (\(M\) = 20 is used [15] for the experiments and discussion in this study) UL packets from the ED based on the prevailing channel conditions. However, the performance of the ADR in terms of the PSR is severely affected by the variable channel conditions [16]. Therefore, the typical ADR suffers from a high convergence period, owing to the time-consuming process of both ED- and NS-managed ADRs. The typical ADR scheme requires several hours to days to converge into a stable and energy-efficient communication state when the NS starts monitoring the \(M\) packets. During the convergence period, the typical ADR suffers from a massive packet loss caused by interference and packets arriving under the pre-defined sensitivity at the GW.