# Adaptive Parameters for LoRa-Based Networks Physical-Layer
## 2 Related Work  
Related research on this topic comprises: **(i)** empirical studies that evaluate performance and other quality attributes of resource allocation strategies ([PERSON] et al. [19]), **(ii)** proposals of research allocation strategies ([PERSON] et al. [5], [PERSON] et al. [20], [PERSON] et al. [11], [PERSON] et al. [21], and [PERSON] et al. [8]), approaches ([PERSON] and [PERSON] [22]), methods ([PERSON] et al. [23], and [PERSON] et al. [24]), techniques ([PERSON] and [PERSON] [25], and [PERSON] and [PERSON] [14]), and algorithms ([PERSON] et al. [17], and [PERSON] et al. [26]), and **(iii)** studies reporting both proposals of resource allocation strategies and empirical evaluation results ([PERSON] et al. [5], [PERSON] and [PERSON] [22], [PERSON] et al. [23], [PERSON] et al. [27], and [PERSON] et al. [28]). The contributions of each one of these studies and a comparative analysis are detailed in the following. [PERSON] et al. [19] conducted an experimental evaluation on how Spread Factor (SF) configuration impacts the network performance in an urban area. They found that the network's scalability increases as soon as the SF is set to provide a lower message's air time, which has less frequency spectrum usage. SF is helpful in increasing scalability and could be used to change the LoRa's parameters dynamically. However, despite the promising results, the study did not demonstrate the effectiveness of the proposed technique in a realist environment (i.e., only running the proposal in a simulated environment). [PERSON] et al. [5] proposed EXPLoRa-C, a novel resource allocation strategy that considers the spreading factor balancing, the channel capture effect, and network topology to make resource allocation decisions. The authors evaluated the performance of EXPLoRa-C and existing resource allocation mechanisms on LoRa networks considering both perfect and imperfect channel orthogonality for different SF values. In their study, the authors conducted the performance analysis via simulations in both single-gateway and multi-gateway scenarios using data from a realist LoRaWAN network with 268 water meters. The results demonstrate that the EXPLoRa-C resource allocation strategy improves up to 38% of network capacity compared with the Adaptive Data Rate (ADR) allocation strategy, and it seems to be more robust to different operating/load conditions and network topology configurations. [PERSON] and [PERSON] [22] developed an approach to LoRa parameter adaption based on machine learning techniques to improve communication in IoT networks. The authors adapted the existing \(LoRa@FIIT\) algorithm using machine learning-based adaptiveness to network condition changes to achieve energy-efficient communication. The authors conducted a simulation-based experiment to evaluate the proposed implementation of the LoRaFIIT algorithm in a network with end nodes and a gateway connected to a real network server. The results demonstrated significant reduction in the number of collisions for mobile nodes, reducing the channel congestion, and improving energy-efficiency by avoiding re-transmissions. [PERSON] et al. [20] introduced a resource allocation strategy that correlates the RSSI values with the weather conditions. The authors deployed a LoRaWAN network containing one (1) LoRa gateway and eight (8) LoRa end devices on a vineyard area in Italy. The data were collected for approximately 85 days. The end devices generated network traffic, and their RSSI values, measured by the gateway, were recorded together with the data of a weather station. [PERSON] et al. [11] focus on Quality of Service (QoS) and energy efficiency of IoT applications in dense scenarios. They propose the ARPA adaptive priority-aware resource allocation mechanism to support real-time adjustment of radio-related parameters to improve scalability and ensure QoS of IoT devices to achieve energy efficiency. The authors evaluated the ARPA resource allocation mechanism via simulation.