A Position- and Energy-Aware Routing Strategy for Subterranean LoRa Mesh Networks
🤖 AI Summary
To address the low bandwidth utilization, high control overhead, excessive redundant retransmissions, and elevated energy consumption caused by ALOHA-based MAC protocols in underground LoRa multi-hop mesh networks, this paper proposes a lightweight, location- and energy-aware adaptive routing strategy. The method achieves low-overhead topology awareness via relative position learning and integrates residual energy balancing with dynamically activated standby relays to enable packet-loss recovery and real-time route switching. Compared to conventional flooding-based routing, the proposed scheme improves throughput by 185% and reduces energy consumption by 75% under typical underground scenarios. These gains significantly enhance both energy efficiency and reliability, establishing a practical, deployable routing paradigm for resource-constrained underground wireless networks.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing throughput and power efficiency in subterranean LoRa mesh networks
Reducing control overhead and redundant retransmissions in low-data-rate networks
Addressing routing challenges with position-aware adaptive strategies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Position-aware routing using lightweight learning phase
Adaptive routing with standby repeaters for collision recovery
Energy-aware route switching to balance battery depletion
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