COSMIC-SWAMP: IoT Processing of Cosmic Ray Soil Moisture Sensors

Cosmic-ray neutron sensing (CRNS) has emerged as a promising technique for non-invasive, field-scale soil moisture monitoring, offering advantages over conventional point-based sensors. However, the practical use of CRNS data in operational smart irrigation systems remains challenging, particularly due to the lack of IoT platforms and frameworks capable of automatically ingesting, calibrating, fusing, and analyzing CRNS measurements in real deployment settings. This paper addresses this gap by presenting COSMIC-SWAMP, an IoT platform and analytics framework designed to operationalize CRNS-based soil moisture data within smart irrigation environments. The proposed framework integrates heterogeneous sensing technologies and communication infrastructures, enabling automated data ingestion, environmental correction, calibration, spatial and temporal data fusion, and forecasting-based analytics at the level of irrigation management zones. A real-world deployment combining LoRaWAN-connected prototype CRNS sensors and a co-located commercial reference system is used as a proof of concept to validate the framework’s feasibility and flexibility under operational conditions. The evaluation demonstrates that COSMIC-SWAMP can harmonize heterogeneous soil moisture data streams, generate consistent volumetric water content estimates, and support automated analytics workflows for irrigation decision support. While this study focuses on system and framework-level validation rather than exhaustive metrological assessment of sensing technologies, the results highlight the potential of COSMIC-SWAMP to enable scalable and sustainable integration of CRNS data into IoT-based smart irrigation systems. Limitations and directions for future measurement campaigns and extended validation are discussed.