# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Throughput
The authors propose a \"region concept\" to assign SFs depending on the RSSI, and within the regions they are allotted SFs corresponding to the proportions provided. The fairness of resource distribution is accomplished by the proposed power allocation scheme, which aims to optimize the RSSI of the end nodes, allocating low TP to the end nodes with a weak signal and high TP to end nodes with a weak signal. They achieve uniform DER for all the end nodes notwithstanding the distance from the gateway and maintain the end node's lifespan by applying low TP levels reducing power consumption by twenty two percent compared to standard ADR. The authors showed that by incrementing the number of end nodes in the network, the fairness index between end nodes decreases. This would favour communication from end nodes nearer to the gateway in comparison with those further away. This approach is therefore only feasible in extremely small networks whose end nodes are positioned near the gateway. Using different propagation models in the simulation is worth investigating. A mathematical model whose numerical analysis results in a closed form formula that maximizes throughput whilst complying with transmission duty cycle (TDC) regulations was proposed in [41]. Their model mitigates against strict restrictions imposed on duty cycle of ISM bands in some regionsand improves transmission and power efficiency in the end nodes. The approach presents two meta-heuristics to solve an optimization problem by computing the transmission policy. An optimal transmission policy means optimal selection of BW, SR, CR and TP. By considering band utilisation and power utilisation efficiency together, the highest achievable throughput of end nodes is calculated and optimized regarding a series of possible transmission parameters, hence achieving the optimal transmission policy. The authors resolve the convolution of LoRaWAN networks by modelling the core status of end nodes applying the Markov model. In so doing, they develop a closed-form formula for node performance which is optimized mathematically with classic maximization algorithms. This method increases performance by over thirty three percent compared to the standard ADR scheme and is indicated to be able to operate in hardware constrained IoT devices. The strength of this approach is that the model is based on an optimal policy derivation theory, which gives the approach the ability to statistically predict the performance of the end nodes in the LoRaWAN network. The drawback is that it is a complex combinatorial optimization problem which cannot be solved directly. Future research could investigate the use of a simpler optimization approach. Reinforcement Learning (RL) based approach is used in [42] wherein they derive efficient ways of disseminating updated transmission configurations to maximize throughput while mitigating against TDC limitations. The authors mathematically model the average throughput per end node as a function of the packet generation behaviour of the end nodes such that the optimal transmission parameters are obtainable. They use the RL-based algorithm to update the configuration of individual end nodes to maximize the accumulated throughput per end node. They then analyse the behaviour of the LoRa nodes focusing on when and how packet collision may occur. Centred on this analysis, they mathematically depict the performance of LoRaWAN as a function of the transmission characteristics of the constituent nodes. The results show a significant improvement in throughput per node compared to standard ADR algorithm. The strength of this approach is that 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 maximising LoRa performance, they update transmission parameters of the overall network configurations. This approach improves throughput but did not look into its effect on power consumption.