# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Scalability
They experimentally observed the capture effect of LoRa, that, where signals with the same SF are transmitted simultaneously, the strongest signal suppresses the weaker signals when the difference in power is adequately large. They developed a LoRa simulator (LoRaSim) and analysed the LoRa scalability threshold in fixed settings. They modelled the capacity of such networks by introducing the Data Extraction Rate (DER) metric, modelling uplink behaviour and proposed a mathematical model for transmission range, dependent on the experimental data gathered. They concluded that the network with a single gateway and moderate transmission parameters did not scale well, whilst those with dynamic adaptation of transmission parameters or multiple gateways tended to scale better. They found that network scalability increases when the parameters configuration minimizes the ToA. This model, however, overestimates link attenuation of LoRa signals in free space. The model needs on-site measurements which are difficult to obtain since most of the area of coverage comprises of conventional connections, classified as connections with dynamic temporal link attributes. Although the use of multiple gateways outperforms existing results, optical placement of the gateways consistent with the category of application would further improve the performance. [PERSON] et al. [34] presented significant improvements aimed at the end node and gateway that decrease the convergence time for LoRa nodes to attain optimum data rate. They extended the LoRaWAN component in NS-3 by including the ADR, thus allowing the simulation of lifelike LoRaWAN networks by implementing novel improvements in this module. The simulations demonstrate a significant decrease in convergence time for the end nodes due to the modifications. This leads to an increase in global PDR for the network in a dynamic network environment. The authors presented an evaluation of the behaviour of the standard ADR scheme and proposed a new variant of the scheme with improvements that enhance performance in all cases whilst maintaining the ability to be efficiently integrated into an existing LoRaWAN network. The authors in [35] employed SF allocation as a tool to increase LoRa network capacity. They defined an optimization problem for SF allocation by maximizing Packet Success Probability (PSP) to maximise end node connectivity. In their scheme, the authors consider both inter- and intra-SF interference and when assigning the SF allocation. The interference considerations are incorporated through the capture effect when the signal of interest maintains a Signal-to-Interference (SIR) which surpasses a certain threshold. The propositioned approach controls the SF distribution dependent on the assignment distances resulting from the solution of the optimization problem instead of the coverage distances derived from the physical layer LoRa sensitivities. The authors use stochastic geometry to determine the average system PSP. They assign SFs to end nodes with two considerations: (a) the received power from the end node must surpass the receiver sensitivity threshold for the allotted SF, and (b) the SIR for the end node must similarly eclipse the accurately decoded SIR threshold for that SF. Although the complexity is high for multiple variables, the global optimization solver can be used to solve the problem. A method to improve the capacity of Mesh LoRa networks using network clustering which is based on SF was proposed in [36]. They used a Tree-Based SF Clustering Algorithm (TSCA) which allocates nodes to numerous subnets. They use the LoRa transmission parameter selection to create mesh networks. The approach is rooted at the gateway in which every tree is a subnet with a different SF to enable simultaneous transmissions. TSCA balances the traffic load to avoid bottlenecks at the subnet using a SF capacity estimation considering the number of nodes, data rates and hop count of the subnets.