Learning the Energy-Accuracy Frontier: Data-Driven Optimization in LoRa IoT Networks

Low-Power Wide-Area Network (LPWAN) technologies such as LoRa are key enablers of large-scale IoT monitoring systems, where long communication range and low energy consumption are essential. These advantages, however, come at the cost of strict throughput limitations, which significantly shape IoT system design. Compressive Sensing (CS) can mitigate this constraint by reducing transmitted data volumes, effectively trading communication load for additional processing at the sensing node and receiver. From an IoT perspective, this shift impacts node lifetime, hardware requirements, and overall network scalability. In this paper, we propose a data-driven optimization framework for LoRa-based IoT sensing systems employing CS. The approach jointly analyzes reconstruction quality and energy consumption through surrogate regression models that capture the interaction between physical-layer parameters and compression levels. This enables efficient multi-objective optimization via Pareto-front analysis and utopia-based selection. Results show that CS does not always dominate the quality-energy trade-off and that unified and stratified surrogate strategies identify closely aligned optimal operating points. Overall, the framework provides a practical and interpretable tool for the design of energy-efficient IoT sensing deployments.