# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Energy Efficiency
More accurate path loss parameters could be estimated using linear regression on measured data instead of sourced data [47] used in the experiments. The deployment environment of the network highly affects the actual path loss parameters. An ADR scheme that efficiently optimizes the PER fairness within a LoRaWAN cell was developed in [48]. The authors achieved this by optimising the TP and SF for every end node whilst circumventing near-far problems by allotting outlying end nodes to different channels. End nodes are arranged by their pathloss and divided into uniform clusters whose number is equivalent to the number of available channels. Every cluster is allocated a distinct channel and inside the clusters the proportion of end nodes employing the SFs is relative to s/2\({}^{\circ}\), corresponding to the solution of the optimization problem to minimize the maximal collision probability among all the SFs. The algorithm calculates the optimal SF allocation to utilize in order to minimize the collision probability. The scheme optimally assigns SFs and distinct power levels to nodes within a LoRaWAN cell such that the signals do not interfere with each other. This scheme improves the PER for end nodes further from the gateway. Simulations of this approach implemented in NS-3 demonstrate that the PER can be reduced by up to fifty percent for end nodes further away from the gateway in a moderate contention scenario. The overall network PER is reduced by 42%. The approach reduces operating expenditure by reducing energy consumption providing wide network coverage which in turn reduces the number of gateways. 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. Further study could investigate randomly distributed end nodes. They consider unacknowledged traffic in this work. Modulation and Coding Schemes (MCS) affect the transmission duration of the data frames. Data delivery time is dependent on network performance which impacts on energy consumption. For example, [49] considers failures in attempts to transmit triggered by arbitrary noise in the channel. The authors develop a precise analytical model of the data delivery time, dependent on the network set up. They show that their model can be used in the MCS (SF, BW, CR) the election process to fulfil heterogenous QoS requirements of different types of traffic. They presented a precise mathematical model of transmitting data which allows estimating performance indices such as PLR payload and delivery time distribution. The strength of this approach is that it considers heterogenous network with various types of traffic and QoS requirements. Furthermore, the proposed model allocates MCSs such that QoS requirements are fulfilled. 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 improved to not only improve the packet loss ratio in the network, but in addition the PLR distribution and to take into account the non-orthogonality of SFs in the model. In [50] the authors develop an approach that improves the QoS of the LoRaWAN network by reducing data collisions and energy consumption, and increasing the DER. They use the Mixed Integer Linear Programming (MILP) optimization method to generate optimum settings for SF and Carrier Frequency (CF) parameters. LoRaSim simulation is employed to demonstrate the effectiveness of the approach for different network scales. For their model, the authors assumed that BW and CR are fixed in computing ToA while they varied CF and SF to maximize the success probability. Three evaluation metrics were used to assess the network performance, namely, DER, number of collisions and network energy consumption.