Environment-Aware Probabilistic Distributed Scheduling for Emergency LoRa Networks

After a large-scale disaster, rapid situational awareness depends on a trustworthy communication infrastructure even when parts of the system are down. LoRa/LoRaWAN, operating in unlicensed sub-GHz bands with long-range low-power characteristics, is a natural candidate for emergency IoT networks. However, its low data rate leads to long airtime, so simultaneous uplinks from many nodes can easily collide, and priority-aware transmission is difficult when centralized coordination is unavailable.

We propose an environment-aware distributed scheduling framework for LoRa-based emergency IoT networks. Each node uses precomputed 3D propagation maps and hazard maps to estimate the approximate positions and priorities of other nodes, and autonomously adjusts its transmission timing, frequency channel, and Spreading Factor to suppress collisions while prioritizing high-urgency traffic. The scheme is formulated as a probabilistic distributed model that jointly handles collision avoidance and priority-aware scheduling.

Simulation on ray-traced urban models shows that the proposed method achieves up to 7× higher throughput than state-of-the-art baselines while keeping reasonable collision rates and ensuring earlier completion of high-priority messages. The results demonstrate that precomputed environmental knowledge — terrain, propagation, and hazard information — enables robust distributed scheduling for small- to medium-scale emergency LoRa deployments.