# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 6 Conclusions  
LoRaWAN allocates resources to the EDs (such as SF and TP) through NS-managed and ED-managed adaptive data rate (ADR) methods. However, the ADR is severely affected by the lack of agility to adapt to the variable channel conditions. Thus, it has a high convergence period and requires several hours to days to converge to a stable and energy-efficient state of communication. During the convergence period, the typical ADR suffers from a massive packet loss caused by interference and packets arriving under the sensitivity at the GW. Therefore, to reduce the convergence period and improve the PSR (for both static and mobile IoT EDs), we proposed two NS-managed ADRs (G-ADR and EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist the rapid changes in the SNR of \(M\) packets. Through NS-3 simulation experiments, we extensively analyzed the proposed schemes as compared to a typical ADR and ADR+. We showed that a typical ADR and ADR+ both  
Figure 13: Average energy consumption for mobile EDs (uplink period = 24 packets/day, total simulation time = 4 days).  
suffer from a high convergence period owing to the time-consuming process and poor adaptation of the SF. In contrast to the typical ADR method, the proposed methods reduce the convergence period, energy consumption, and enhanced the PSR for both static and mobile EDs. We further remark that the proposed methods are highly suitable for IoT-based static and mobile applications requiring a low convergence period, high PSR, and reliability without sacrificing a high energy consumption.  
Conceptualization, A.F., D.-H.K., S.S., and J.-Y.P.; methodology, A.F., and D.-H.K.; software, A.F.; validation, A.F., D.-H.K. and S.S.; formal analysis, A.F.; investigation, A.F., and D.-H.K.; resources, A.F.; data curation, A.F.; writing--original draft preparation, A.F.; writing--review and editing, S.S., and J.-Y.P.: visualization, A.F.; supervision, J.-Y.P.: project administration, J.-Y.P. funding acquisition, J.-Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (2018-0-00691, Development of Autonomous Collaborative Swarm Intelligence Technologies for Disposable IoT Devices). Also, it was supported by \"Smart city urban infrastructure air quality real-time monitoring and prediction platform technology development\" through the Ministry of Trade, Industry and Energy (MOTIE) (P0006855, 2018).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACK & Acknowledgment \\ ADR & Adaptive Data Rate \\ DL & Downlink \\ ED & End Device \\ EMA-ADR & Exponential Moving Average ADR \\ G-ADR & Gaussian Distribution based ADR \\ GW & Gateway \\ I-SFA & Initial Spreading Factor Assignment \\ IoT & Internet of Things \\ LoRaWAN & Long-Range Wide Area Network \\ LPWAN & Low-Power Wide Area Network \\ MAC & Media Access Control \\ NS & Network Server \\ PSR & Packet Success Ratio \\ RX & Receive Window \\ SF & Spreading Factor \\ SNR & Signal-to-Noise Ratio \\ ToA & Time-on-Air \\ TP & Transmit Power \\ UL & Uplink \\ \end{tabular}