# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 4 Comparison and Discussion
The ordered waterfalling technique used in [37] for even distribution of channel load between the end nodes in the system by equally distributing SFs and then channel utilization. This algorithm is designed under the assumption that end nodes transmit with the uniform data rate and payload which is not practical. When we compare it to [39] which uses the gradient projection method we find that their approach also uses the load balancing approach but factors in the contention issue in optimising ADR making it a better scheme in maximizing throughput. The algorithm in [40] uses all four transmission parameters to maximize the DER and attempts to fairly allocate data rates. The algorithm works with nodes close to the gateway, making the solution suitable only for small networks. The algorithm in [42] it takes into consideration the fact that IoT networks are heterogenous, that send packets at different packet rates and varying payloads. In the model they consider randomly distributed end nodes and the capture effect. Instead of just maximizing LoRa performance, they update transmission parameters of the overall network configurations. Network slicing brings an interesting dimension in ADR optimization which sets the LoRa SF and TP parameters efficiently to improve the performance of every slice in terms of QoS. In our opinion the algorithm in [45] provides the best approach for maximizing throughput, outperforming standard algorithms, jointly with regards to minimum end node data rates, user fairness, and average end node throughput. The challenge of conserving energy in LoRaWAN networks is a QoS in the ADR algorithms in [20; 46; 47; 48; 49; 50; 51]. In [20] they first optimize every end node to minimise the ToA, which is determined by its distance from the gateway, searching for optimal gateway location ensuring all nodes are connected. They then use the results of the first optimisation to do a second optimisation that minimizes energy consumption. Although the results show that optimisation can yield higher PDR and lower energy consumption, interference from other ISM frequency users was not considered in this approach, yet it could be a crucial factor which affects the performance. In [46] they also optimise two parameters, first they optimise SF allocation to ensure reliable communication and then optimise TP to minimise energy consumption in the network. The approach guarantees that a significant proportion of end nodes communicates reliably with a high PDR. All the network end nodes share the improved PDR thus guaranteeing an unbiased distribution of RF resources to all the end nodes. It is a better approach compared to [20] because the models are general, thus allowing network configuration with single or multiple gateways, along with different spatial configurations of LoRa end nodes. It is a more practical approach. In [48] The scheme uses uniform distribution of the end nodes around the gateway and all the end nodes can use all SFs and TPs, that is, all end nodes in the network are capable of reaching the gateway with each SF and each TP configuration. This does not function in actual networks where specific end devices can only utilise a subset of the configuration parameters determined by the distance from the gateway. In [49] they use an optimisation approach with three transmission parameters, SF, TP and CR, to develop an analytical model for heterogenous network with various types of traffic and QoS requirements. The algorithm has a drawback in that it does not consider inter-SF interference which is vital for huge network loads. The model can be enhanced to improve the packet loss ratio in the network and energy consumption. In [50] they optimise the ADR scheme using SF and CF to maximise the success probability while fixing BW and CR to compute ToA, improving the DER, thus conserving energy.