# Adaptive Parameters for LoRa-Based Networks Physical-Layer
## 2 Related Work
The simulation results demonstrated a 95% of improvement in the energy efficiency of IoT devices, with higher rates of packet delivery and reduced delay in high-priority applications. [PERSON] et al. [23] proposed an adaptive resource allocation based on mixed-integer linear programming to define the best LoRaWAN parameter settings to reduce channel utilization and maximize the number of packets delivered. The authors evaluated their solution against resource allocation heuristics. The results demonstrated the effectiveness of resource allocation heuristics, achieving results nearest to the optimal solution provided by integer linear programming. [PERSON] et al. [21] present a scheme to minimize the packet collision probability, especially in places where devices are distant from the gateway. The authors evaluated their solution in a simulation whose results demonstrate that the packet error rate can be decreased up to 50%. [PERSON] et al. [17] proposed an algorithm that optimizes the transmission power and SF in a way that balances the transmission data rate on the network, assuming that all nodes are able to reach the gateway. Different from the aforementioned studies, [PERSON] and [PERSON] [25] proposed adaptive changes on both the transmitting power and **up-link** data rate based on the **observed** performance and **enhancements** in the upload channel. [PERSON] et al. [8] proposed an adaptive network to switch between LoRaWAN and IEEE 802.11 ac [29] protocols in situations of lower or higher data transmission demands, respectively. However, their study did not provide any suggestions regarding the performance enhancement of LoRa's parameters. The authors highlighted that the Packet Error Rate (PER) for IEEE 802.11 ac long-range transmissions are substantially higher than LoRa's medium and long-range transmissions. [PERSON] et al. [24] propose an adaptive mechanism called FLoRa to configure the main parameters of LoRa networks, focusing on dense IoT scenarios. The authors evaluated the adaptive data rate from LoRa in a synthetic simulation scenario, showing the benefits of using a mechanism that observes and changes the LoRa parameters settings. Other related work evaluated by [24] is [PERSON] et al. [26], where the authors present two different algorithms called Gaussian-based Adaptive Data Rate (GADR) and Exponential Moving Average-based Adaptive Data Rate (EMA-ADR). The results are promising concerning the packet success ratio, energy consumption, and convergence period compared to [24]. [PERSON] et al. [28] propose a linear regression extension of Adaptive Data Rate (LR-ADR) resource allocation mechanism for the network server side to smooth SNR per gateway and to support LoRa-enabled end-devices to regain the connectivity with the network server faster. They conduct an empirical study to evaluate LR-ADR performance compared to ADR, ADR, EMA-ADR, and G-ADR resource allocation mechanisms. Their results demonstrate that LR-ADR has better performance, an improved Packet Delivery Ratio (PDR), and kept the Energy Consumption per Packet Delivered (ECPD) at lower levels than the aforementioned alternative solutions. The resource allocation mechanism proposed in our work supports the dynamic adjustment of LoRa network performance parameters based on a sliding window to smooth short-term variations and reduce unnecessary network reconfigurations. Our solution focuses on improving SNR, Bit Error Rate, and the number of frequency switches LoRa network performance parameters, while LR-ADR [28] and EMA-ADR solutions focus on improving packet delivery ratio and energy consumption per packet delivered parameters. So, both resource allocation mechanisms can be seen as complementary to each other. To enrich this article, a comparison between our proposal and LR-ADR is present in the evaluation section. [PERSON] and [PERSON] [14] propose a technique to enhance the efficiency of a LoRa network with practical implications.