Title: A conflict-balanced SF allocation for a LoRa network

Authors: Junming Guan; Yancheng Yao; Chuanxin Zhao; Changzhi Wu

Addresses: School of Information Engineering, Huangshan University, Huangshan Technology Innovation Center for Digital Economy and Big Data Analysis, Huangshan – 245000, Anhui Province, China ' School of Computer and Information, Anhui Normal University, Wuhu – 241000, Anhui Province, China ' School of Computer and Information, Anhui Normal University, Wuhu – 241000, Anhui Province, China ' National Center for Applied Mathematics, Chongqing Normal University, Chongqing – 401331, China

Abstract: Long range (LoRa) has emerged as one of the preferred communication solutions for connecting internet of things (IoT) devices due to its low power consumption and long-distance transmission capabilities. The LoRa network supports communication between multiple nodes and gateways over several kilometres. To reduce energy consumption and enhance the communication quality, the configuration of LoRa network is important. In this paper, the signal communication in a LoRa network is first analysed, and a nonlinear integer programming problem is developed to configure the spreading factor (SF) of nodes. The problem is formulated by maximising the volume of successfully transmitted data and reducing the probability of data collisions to minimise system energy consumption. Additionally, the performance of the proposed scheme is evaluated through extensive simulations. The results demonstrate that it achieves a higher number of delivered packets with lower energy consumption, outperforming both the equal-interval-based (EIB) and equal-area-based (EAB) SF allocation mechanisms. Finally, the network scalability is analysed by considering factors such as network configuration time, node density, and network radius.

Keywords: LoRa networks; spreading factor; allocation scheme; equal-area-based; EAB; equal-interval-based; EIB.

DOI: 10.1504/IJSNET.2025.149901

International Journal of Sensor Networks, 2025 Vol.49 No.3, pp.171 - 182

Received: 18 Aug 2024
Accepted: 01 Jul 2025
Published online: 17 Nov 2025 *

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