01714nas a2200181   4500000000100000008004100001100001900042700001600061700001700077700001600094700001800110245010800128856004600236300000900282490000700291520122000298022001401518       2025                            d1 aCarles Aliagas1 aRoger Pueyo1 aRoc Meseguer1 aPere Millan1 aCarlos Molina00aDynamic Selection and Detection of Spreading Factors and Channels for End-Node Devices of LoRa Networks  uhttps://www.mdpi.com/2079-9292/14/17/3341  a33410 v143 aLoRa and LoRaWAN facilitate effective long-range communication for IoT, with LoRa concentrating on transmission efficiency and low-power usage, while LoRaWAN addresses network architecture. Custom LoRa networks are ideal for small-scale applications due to their control and cost benefits. Nevertheless, large-scale deployments can experience message collisions, impacting efficiency and latency. LoRaWAN addresses this with Adaptive Data Rate (ADR), enhancing capacity and power efficiency. Our research introduced a novel strategy to improve LoRa network efficiency through a decentralized method. We employed multiple channels and spreading factors on client chips. This minimized contention and collisions. This approach allowed for dynamic adjustments, ensuring comprehensive communication control and enhanced performance in diverse environments. Our two-step mechanism, integrating heuristics and selection policies, provided flexible communication. We optimized parameters such as message size, transmission power, and bandwidth. This enhanced data rate, RSSI, and SNR, and reduced energy consumption. These results underscore the relevance of precise parameter tuning in achieving optimal LoRa performance.  a2079-9292