# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 4 Comparison and Discussion  
In this paper, we performed a comprehensive analysis of solutions that have been developed to optimize ADR algorithms. The proposed optimization techniques address specific challenges such as scalability [28; 30; 31; 32; 33; 34; 35; 36], throughput [37; 38; 39; 40; 41; 42; 43; 44;  
the adaptive slicing and SF-TP configuration algorithm has a constant complexity of \(O(1)\) for the static algorithm owing to its simplicity. However, the overall complexity of the proposed dynamic adaptive slicing and SF-TP algorithm and TOPG algorithm is \(O\left(n^{2}\right)\). Complexity is minimised in TOPG due to the server reducing the search space to SF values that acknowledge the guaranteed bit rate threshold. The computation time is reduced without a significant effect on QoS performance. In [45] the running time of the developed SF-Allocation algorithm is upper bounded by \(O\left(NM+Q^{2}+M^{2}\right)\). The complexity of the matching algorithm is not a constraint in an actual LoRaWAN because the algorithm operates on the NS whose computational capacity is expansive. In [48] the number of end nodes was capped at 1000 because of the restricted memory of the computer. All the transmission parameter needed to be broadcast to the end nodes resulting in a \(O\left(n^{2}\right)\) memory consumption. In [50] the Approximation Algorithm maintains a linear complexity time \(O(n)=111n+57\) in the worst-case. The algorithm is designed to function in the LoRaWAN Application Layer and end nodes with a time complexity that is equivalent to the ADR scheme, so that the proposed optimization algorithm does not cause any substantial computation overhead, neither in the end nodes nor in the NS. Generally, the operation of the algorithm uses less than 20 kB of memory, 4 kB on average and 20 kB being the worst-case. This is insignificant since most commercial off-the-shelf end nodes contain no less than 128 kB of flash memory [52]. The algorithms used to improve scalability in LoRaWAN were discussed in [30; 31; 32; 33; 34; 35; 28; 36]. The strength of the approach in [28] is that it considers the level of congestion in the network unlike the legacy ADR scheme. The drawback of this approach is that the process demands an ACK DL message for each transmission. Although the proposed algorithm in [30] shows significant increase in reliability and scalability, there is a drawback. Waiting for twenty frames in order to adjust the scheme may be too long. For dense networks, link-based adaptation is inadequate. Future work could consider incorporating collision probability and the distribution of parameters in the network. Balancing the link budget for every link and PDR of the entire network could further improve scalability. In [31] although the light-scheduling approach improves reliability and scalability, there is an introduction of additional energy consumption as the end nodes need to listen for the beacon from the gateway before sending a packet. The approach does not eliminate packet collisions entirely because uplink messages could still collide with beacons from other gateways. Because the approach uses Aloha, it also means collisions cannot be eliminated. The proposed algorithm in [32] reduces the number of data messages in the UL, as well as the MAC command messages in the DL, and achieves superior performance in poor condition channels. This enables the extension of the network range. The drawback of this algorithm is that the simulation model used does not consider large complex networks. The use of a Tree-Based SF Clustering Algorithm (TSCA), which allocates nodes to numerous subnets in [36], vastly improves scalability as is the characteristic of mesh networks. In our opinion, the algorithm in [35] is the best approach in improving scalability as the proposed solution outperforms the equal-interval and equal-area based SF distribution schemes in terms of average network PSP. ADR schemes that maximize throughput maximizing solutions for the ADR scheme were discussed in [37; 38; 39; 40; 41; 42; 43; 44; 45].