Enhancing Scalability of LoRa Networks in Smart City Conditions: A Machine Learning Approach

Abstract

The Internet of Things (IoT) is a rapidly expanding network of interconnected devices. Low Power Wide Area Network (LPWAN) technologies un- der the umbrella of IoT enable cost-effective long-range communication. Among LPWAN technologies, LoRa has gained prominence as a leading unlicensed-band IoT technology, particularly suited for smart city de- ployments due to its ability to transmit over several kilometers with minimal power consumption and low data rates. However, in densely populated urban set- tings, LoRa devices transmit independently, often lead- ing to packet collisions and degraded network perfor- mance. This study introduces an optimized approach to configuring LoRa communication parameters to im- prove scalability, reduce collisions, and enhance data transmission reliability. We propose a stochastic gradi- ent descent (SGD)-based optimization method, achiev- ing a 2–12 % increase in delivery ratio across net- works ranging from 200 to 2000 devices. Additionally, we evaluate and compare existing optimization strate- gies, including MinSF, ADR, and ADR+, while refin- ing Spreading Factor (SF) allocation to mitigate inter- ference. The findings highlight a significant enhance- ment in LoRa network efficiency, making it more reli- able for large-scale smart city applications.