Enhancing Link Performance for Mobile LoRa Networks

Ciyuan Chen, Zhuqing Xu, Runqun Xiong, Dian Shen, Weizheng Wang, Junzhou Luo, Xiaohua Jia

Abstract: LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is significant packet loss in LoRa transmission due to link performance degradation. Existing studies take little account of end-devices’ movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance the Link Performance for Mobile LoRa networks in general scenarios for both single-gateway and multi-gateway applications. The key observation is that, due to LoRa’s unique feature, repeating the original packet content enables the use of smaller, more energy-saving transmission parameters, which not only enhances link performance but also reduces energy consumption. Technically, we propose a link performance estimation model based on packet content repetition for both single-gateway and multi-gateway mobile networks. Then, we propose the corresponding channel frequency selection model to avoid transmission collisions. Finally, we design low-overhead communication mechanisms to operate the system. To evaluate the performance of LMLoRa in various scenarios, we design and implement real-world testbeds and a simulation platform for both single-gateway and multi-gateway scenarios. Extensive results show that LMLoRa improves packet delivery ratio by an average of 33.4$\%$ to 69.2$\%$ compared with the state-of-the-art.

External IDs:dblp:journals/tmc/ChenXXSWLJ25