Evaluation of an IoT Strain-Sensing System with LoRa Telemetry and Asset Metadata

Instrumented rock-bolt monitoring requires strain records that retain integrity and asset context after local processing and wireless transmission. An Internet of Things (IoT) strain-sensing system with LoRa telemetry and asset metadata was evaluated on a cantilever fixture. The node converted tare-referenced bridge counts to apparent strain, applied temperature compensation using a lagged temperature term, evaluated alarms locally, and buffered summaries for LoRa transfer. Histories were assessed using sequence continuity and a 32-bit cyclic redundancy check (CRC-32). Ten monotonic loading runs showed linear responses to nominal screw advance (R² > 0.9997); fitted slopes had a coefficient of variation of 0.55%. Across three fixed-setting tests spanning 5.1-8.0 °C, unchanged embedded compensation reduced the magnitude of fitted apparent thermal sensitivity by 89.1-95.5%. In all 45 operator-controlled alarm trials, trigger or non-trigger outcomes matched expectations recorded before dashboard inspection. Of 60 planned application-layer history transfers, 50 passed; the remaining ten comprised four failures, three interruptions, and three invalid or contaminated trials. Passed transfers included exact reconstruction of a 1024-record circular buffer. Retrieved metadata passed CRC-32 verification and matched the registered laboratory asset. These results characterize a one-asset laboratory workflow before packaged-bolt, underground, and multi-asset validation.