## 6 Conclusions  
A systematic literature review has been conducted to evaluate the performance of LoRaWAN technology and how the use of ML and AI can enhance overall network efficiency. The SLR has highlighted the key role of advanced algorithms in optimizing resource allocation, managing network parameters dynamically, and adapting to diverse environmental conditions. It also addressed some of the critical challenges faced by LoRaWAN deployments. The review followed the PRISMA model for selecting studies that particularly focused on LoRaWAN and ML.  
The extensive literature review has been conducted, and key findings have been observed and stated as follows:  
* DRL, and supervised learning techniques have shown efficiency and improvements in refining energy consumption strategies.
* These methods have ensured robust network connectivity across diverse environments and enhanced the sustainability of LoRaWAN systems. They have also extended the operational life of battery powered IoT devices, which is a crucial factor for large-scale deployments.  
Various key parameters are optimized to ensure better performance, specifically the SF. SF is crucial for determining communication range, data rate, and energy consumption.  
It determines how data is spread across the spectrum and has a direct influence on the signal's robustness and network capacity. SF also influences network capacity and collision rates. In dense network deployments, the choice of SF can lead to increased collisions if not managed properly.
* Adaptive algorithms, including RL, are used in the literature to optimize the SF according to real-time network conditions to mitigate collisions and optimize network performance.
* Various sophisticated machine learning models have also been developed by the studies to dynamically adjust SF to reduce collision rates and improve overall network reliability.
* The adjustments in BW have also been addressed. It has impacted the data rate and energy consumption, where wider bandwidths increase the data rate but also raise the power requirements.
* In contrast, optimization of CR determines the redundancy in data transmission and enhances error correction capabilities at the cost of increased message length.  
SLR has reviewed how the state-of-the-art has focused on dynamically balancing these parameters to optimize network throughput and reliability, especially in variable environmental conditions. Advanced algorithms, often in integration with SF adjustments, have dynamically optimized BW and CR to maintain optimal performance. This is crucial for adapting to network load and channel quality to minimize packet loss and improve link stability.  
Future work will focus on reviewing real-time adaptive systems that use ML to dynamically adjust network parameters. Future work also aims to explore cross-layer design strategies that consider interactions between different layers of the network protocol stack to optimize overall network performance.  
Conceptualization, M.A.; methodology, M.A.; software, M.A.; validation, M.A.; formal analysis, M.A.; investigation, M.A.; resources, M.A.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A. and A.M.; visualization, M.A.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.