Towards dependable IoT in remote environments: a stochastic Petri net model for energy-harvesting LoRaMesh router nodes

Remote sensor deployments often suffer from limited maintenance and connectivity issues due to adverse environmental conditions. Consequently, sensors are frequently in short supply, resulting in critical gaps in environmental data collection. Low-Power Wide-Area Network (LPWAN) protocols address these challenges by enabling long-range, low-energy communication. In this context, LoRaMesh networks rely on autonomous router nodes to maintain multi-hop connectivity in remote environments. Therefore, the dependability of individual nodes becomes a critical factor for sustaining reliable network operation. To this end, we develop a formal modeling approach using stochastic Petri nets (SPN) to evaluate the dependability and energy-driven availability of an autonomous LoRaMesh router node equipped with multiple energy sources. The results indicate that the modeled node maintains steady-state availability above 98%, with the battery serving as an effective buffer even under cloudy conditions. Sensitivity analysis reveals that a 50% reduction in solar charging capacity can decrease availability by up to 15%. These findings highlight the impact of environmental factors on hardware reliability and provide design guidelines and quantitative assurance for prototyping resilient LoRaMesh router nodes in unpredictable remote environments.