# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 2 Related Studies
### Enhancements in Typical ADR  
In [19], an approach to avoiding unnecessary changes to the SF that occur at the NS-managed ADR is presented. Their method offers a congestion classifier, which determines whether to switch to a higher SF or adjust the back-off time to avoid massive congestion. The congestion classifier is based on UL and DL packets. If the number of DL packets received by the ED is equal to the number of UL packets, then no congestion is shown. Otherwise, the SF is reduced because a higher SF is vulnerable to interference owing to a high time-on-air. When an ED fails to receive an ACK after \(ADR\_ACK\_DELAY\), it chooses a long back-off time. By contrast, it increases the SF to extend the network scalability and coverage. The authors showed that their method reduces the delay because the ED maintains the SF and increases the back-off time when congestion occurs.  
An ADR+ scheme was proposed in [20] in order to improve the ADR performance. The ADR+ scheme slightly modifies the NS-managed ADR by taking the average SNR of the last \(M\) packets received. Thus, the ADR+ plays a vital role in increasing the consistency and energy efficiency of EDs under variable channel conditions. ADR+ shows an improved performance in terms of PSR and energy consumption when compared to a typical ADR.  
It should be noted that a typical ADR is inadequate and inefficient when the EDs are mobile, because it manages the SF and TP after the reception of \(M\) packets [21]. In addition, the typical ADR does not consider the degradation of the received signal that is caused by the ED mobility, the presence of building penetration loss, or moving objects. Therefore, Ref. [21] suggests a network-managed enhanced ADR (E-ADR). The E-ADR is primarily based on the trilateration technique to estimate a mobile ED's next position with a pre-defined trajectory. An experiment using E-ADR as compared to a typical ADR was conducted by applying five EDs and three GWs in a testbed scenario. The authors claim that NS quickly defines SF and TP, which results in low energy consumption and packet loss.  
In [22], the authors suggest another enhanced ADR with coding rate adaptation in the LoRaWAN under the unconfirmed mode of LoRaWAN. The primary aim is to improve the tradeoff between the PSR and energy consumption, which considers the coding rate and the capture effect, by taking the average of \(M\) packets to fine-tune the link performance of the EDs. The method that is described in [22] outperforms a typical ADR in terms of energy consumption and fairness.  
By summary, the methods that are presented in [19; 20; 21; 22] enhanced the performance of a typical ADR. The congestion classifier-based method described in [19] is only focused on improving the data rate adaptation. However, it ignores the TP adaptation because the energy consumption in LoRaWAN is mainly based on the TP and the SF [23]. In [20], ADR+ was evaluated only in a small environment for static EDs. E-ADR described in [21] was considered for mobile EDs; however, it is solely based on a pre-defined trajectory of the ED. Finally, the method in [22] was compared to a typical ADR in unconfirmed mode. Note that a typical ADR performance is significantly affected by bi-directional communication supporting both the UL and DL. Keeping these facts in mind, we propose two NS-managed ADRs to enhance ADR performance in terms of the convergence period, PSR, and energy consumption.