Energy-Efficient Multi-Hop LoRa Communication in Forested Environments via Proximal Policy Optimization

Multi-hop LoRa networks extend coverage for large-scale Internet of Things deployments but are severely limited by interference-induced collisions, retransmissions, and rapid battery depletion of relay nodes. Conventional routing strategies that minimize hop count or rely on static heuristics fail to account for dynamic medium contention and its impact on energy consumption and reliability. This paper proposes a Proximal Policy Optimization (PPO)–based routing framework for multi-hop LoRa networks that learns interference-aware and energy-efficient routing policies through reinforcement learning. A discrete-event simulation framework is developed to model LoRa physical-layer behaviour, co-spreading-factor interference, adaptive data rate control, and battery-limited relay nodes under multi-source traffic. The routing problem is formulated as a Markov Decision Process (MDP) in which the PPO agent selects next-hop relays based on local topology, relay load, and channel occupancy, while physical-layer parameters are adapted independently using a standards-inspired physical-layer parameters are adapted independently using a standards-inspired ADR mechanism (ADR) mechanism. Simulation results show that the proposed approach achieves a packet delivery ratio of up to 73.7%, reduces collision rates by approximately 46% compared with Random routing, and lowers the average energy consumption per delivered packet to about 206 mJ, outperforming Shortest Path and Ad hoc On-Demand Distance Vector (AODV)-like routing. These gains are achieved by learning spatially diverse routing paths that mitigate relay congestion and reduce collision-induced retransmissions.