# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Scalability  
In LoRaWAN networks, ADR control is enabled to keep network connectivity by providing numerous data transmission rates. The condition of the RF connection is determined through the receive status of the ACK packet in LoRaWAN through link errors. This, however, does not indicate a congested network but may result in inefficiencies in transmitting data due to long transmission delays. Since congestion is not considered a connectivity problem, decreasing the data rate by substituting the modulation system is inappropriate. In [28] the authors developed an ADR scheme that attempts to preclude extraneous data rate management using logistic regression. The proposed scheme recognises the congestion levels of the network by learning and then applying the result to control the data rate. The method uses data rate, RSS, and number of connections at the gateway as characteristics for learning. Evaluation of these attributes provides the congestion estimation. When the congestion is projected, the proposed algorithm revises the back-off time rather than reducing the data rate, adjusting latency instead of reducing throughput. This results in an improvement in the accuracy of data rate control and network efficiency. The NS performs the learning and delivers the results to the end nodes to predict congestion. The computation for learning is done in a centralised machine. The strength of this approach is that it considers the level of congestion in the network unlike the legacy ADR scheme. The drawback of this approach is that the process demands an ACK DL message for each transmission. Because DL traffic has a negative effect on UL throughput, the PDR decreases as a result [29]. Future research could look at studying distributed learning of the end nodes. Other methods of optimization could be applied for predicting congestion. Framework for LoRa (FLoRa) was developed in [30], wherein the authors dynamically manage link parameters for scalable and efficient network operations. The authors developed a non-proprietary scheme for end-to-end LoRa simulation in OMNeT++. They considered a distribution of SFs on the average SNR values from several uplink packets received at the gateway as opposed to the highest SNR used in the standard ADR scheme. They use comprehensive simulations to demonstrate that ADR effectively increases the PDR, maintaining low energy utilization under stable RF conditions. Their results showed that adjusting channel conditions had a severe effect on the performance of the ADR scheme, hence the modification of the link quality indicator, and the introduction of a transmission policy to increment TP at the end nodes. The proposed ADR scheme achieved at least thirty percent better PDR compared to the standard ADR scheme in the cases of moderate variable channel conditions. The proposed scheme showed that appropriately configuring SFs and TPs could boost the network capacity and reduce energy utilization, based on the overall knowledge of the network. It implements an ADR algorithm wherein end nodes could dynamically update their SF and TP. Although the proposed algorithm shows significant increase in reliability and energy efficiency, there is a drawback. Waiting for twenty frames in order to adjust the scheme may be too long. For dense networks, link-based adaptation is inadequate. Future work could consider incorporating collisionprobability and the distribution of parameters in the network. Balancing the link budget for every link and PDR of the entire network could further improve scalability. [PERSON] et al. (2018) proposed a MAC layer protocol RS-LoRa which distributes SFs and TPs to reduce the capture effect and inter-SF collisions and improve network reliability and scalability. They introduced a two-step lightweight scheduling method that divides end nodes into clusters, where identical TPs are applied in each cluster to diminish the capture effect.