# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Energy Efficiency  
Authors in [20] evaluated the performance of LoRaWAN under different scenarios using computer simulation and testbeds. They investigate the network performance regarding PDR, mean energy usage per transmission and mean energy utilization due to packet collision per node. They show that the LoRa network performance is enormously reliant on to the configuration of SF, CR, and BW. The experiments show the access strategy of pure Aloha has limitations which affect performance. The short-coming could be mitigated by the randomization of BW selection and node-specific optimization to maximize data rate in relation to its channel conditions and contention severity. They conduct several experiments, the first which considers robustness and uplink data rate which they evaluate with and without the capture effect. In the second experiment they incorporate multiple frequency randomisation to minimize the collisions. In this experiment, they adopted the slowest case from Experiment one. The results show that the frequency randomization that was introduced is capable of increasing the PDR of the slowest case. In the final experiment they contrast two simple optimizations. The first optimization, Opt1, sets the parameters in every end node so as to minimizes ToA, which is determined by its distance from the gateway. The second optimization, Opt2, sets the parameters in every end node so that it minimizes its ToA and energy consumption. In other words, Opt2 attempts to minimize the energy utilization of the solution attained by the Opt1. The results show that optimization can yield a higher PDR and lower energy consumption. Interference from other ISM frequency users was not considered in this approach but could be a crucial factor which affects the performance. This could be interesting for future research. It could also be noteworthy to examine the effect of the different classes of the end nodes and their DL transmissions including the presence of multiple gateways in the vicinity. In [46] the authors aim to optimally transmit data with a high PDR whilst maintaining a low energy utilization. They introduce integer linear programming models to establish the optimum allocation of the parameters by considering the distribution of the end nodes and the impact of transmissions from the surrounding end nodes. They split the optimization into two phases. First, they optimize the SF such that the collisions in the SF with the most traffic are marginal, and the collisions in every SF is balanced for all gateways to guarantee reliable communication within the network. As the problems in consideration are non-linear, they are converted into tractable integer linear programming models. Secondly, they optimize the TP to minimize the energy consumption in the network. After obtaining the optimum configuration, they analyse the solutions by means of extensive network simulations and compare them to different state of the art algorithms. The results illustrate that the optimal configurations consistently perform better, achieving a greater PDR and nominal energy utilization across different scenarios. The approach can guarantee 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. The strength of this approach is that the models are general, thus allowing network configuration with single or multiple gateways, along with different spatial configurations of LoRa devices. Commercially available solvers can be used to solve the optimization model within a short space of time. This means that even for huge and dense networks with thousands of devices, the proposed model can be readily employed by service providers to establish the optimum setting of the LoRa parameters. The dynamic reconfiguration of the LoRa parameters could be an open area for research.