# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Scalability
First, the end nodes get recommended by the gateway's coarse-grained scheduling to apply distinct SFs and RSS to allow concurrent transmission, therefore reducing packet collisions. The gateway broadcasts beacons to the end nodes before the nodes can send any packets. Depending on the coarse-grained information supplied by the gateway, the end nodes select the parameter combination and channel that best suits the nodes. Using this approach, reliability of the network is improved by reducing packet error rate (PER) up to twenty percent compared to standard LoRaWAN using NS-3 simulation. Improved network reliability improves network scalability which in turn contributes to an improvement in the throughput. Network performance improves significantly when there are many end nodes in the network to the tune of one thousand, for example. Although the light-scheduling approach improves reliability and scalability, there is an introduction of additional energy consumption as the end nodes need to listen for the beacon from the gateway before sending a packet. The approach does not eliminate packet collisions entirely because uplink messages could still collide with beacons from other gateways. Because the approach uses Aloha, it also means collisions cannot be eliminated. Future work could look at using a different MAC protocol to improve PER. The authors in ([PERSON] et al., 2018) modified the standard ADR algorithm to produce an expansion of the network scalability, fairness between nodes, PDR and robustness to dynamic channel conditions. The algorithm facilitates the system to optimize SF and TP for each end node with the purpose of increasing reliability of communication and optimizing energy usage at the end nodes. The authors recommend varying data rates before increasing TP, averaging of SNR history, and accounting for hysteresis. With empirical evaluation of the relationship between PER and SNR the authors find a channel model which they subsequently applied in MATLAB simulations. Certain alterations of the algorithm, enable the achievement of improved error performance in rough channels resulting in the reduction in the amount of retransmissions and DL commands from the NS. The algorithm decreases the data rate before increasing TP whenever a given fixed threshold is exceeded. They introduce variable hysteresis into the algorithm which mitigates against the effect of collisions and duty cycle limitations. The proposed algorithm reduces the number of data messages in the UL as well as the MAC command messages in the DL and achieves superior error performance in poor condition channels. This enables the extension of the network range. The drawback of this algorithm is that the simulation model used does not consider large complex networks. A model that optimizes attenuation and collisions in order to optimally allocate the SF was proposed in ([PERSON] et al., 2018). The model aims to optimize the quantity of end nodes distributed in the network whilst assigning SFs that maximize transmission quality. They consider physical capture and imperfect SF orthogonality whilst ensuring a specified probability of successful transmission to every end node within the network. The approach considers an authentic propagation model which considers physical capture that could surface at the gateways. They consider inter-SF and intra-SF interference for each node as a potential for packet collisions. However, it assumes that the density of the end nodes within the gateway range is uniform, which is not realistic. End nodes covering areas of several square kilometres would be highly non-uniform. This mathematical model needs to be validated by simulation to quantify the gains of appropriate SF allocations regarding PDR in a realistic environment. [PERSON] et al. proposed a dynamic transmission scheme and made the network denser by adding more gateways.