Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa's low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently utilize the limited available network bandwidth in these low-data-rate networks due to excessive control overhead, acknowledgments, and redundant retransmissions. In this paper, we introduce a novel position- and energy-aware routing strategy tailored for subterranean LoRa mesh networks aimed at enhancing maximum throughput and power efficiency while also maintaining high packet delivery ratios. Our mechanism begins with a lightweight position learning phase, during which LoRa repeaters ascertain their relative positions and gather routing information. Afterwards, the network becomes fully operational with adaptive routing, leveraging standby LoRa repeaters for recovery from packet collisions and losses, and energy-aware route switching to balance battery depletion across repeaters. The simulation results on a representative subterranean network demonstrate a 185% increase in maximum throughput and a 75% reduction in energy consumption compared to a previously optimized flooding-based approach for high traffic.

翻译：尽管LoRa主要与单跳的LoRaWAN协议配合使用，但近期的进展已将其应用扩展至多跳网状拓扑结构。由于LoRa的低数据速率和基于ALOHA的MAC协议，为LoRa网状网络设计高效路由仍具挑战性。先前的研究通常针对具有严格占空比约束的低流量地面网络调整传统协议，或在地下环境中使用基于泛洪的方法。然而，这些方法因过多的控制开销、确认消息和冗余重传，未能有效利用这些低数据速率网络中有限的可用带宽。本文提出一种专为地下LoRa网状网络设计的新型位置与能量感知路由策略，旨在提升最大吞吐量与功率效率，同时保持高数据包投递率。我们的机制始于轻量级位置学习阶段，在此阶段LoRa中继器确定其相对位置并收集路由信息。随后，网络通过自适应路由完全投入运行：利用备用LoRa中继器实现数据包碰撞与丢失的恢复，并采用能量感知路由切换以平衡各中继器的电池损耗。在典型地下网络上的仿真结果表明，相较于先前针对高流量优化的基于泛洪的方法，本策略使最大吞吐量提升185%，能耗降低75%。