LoRa Communication for Inclination-Based Structural Health Monitoring: Experimental Performance and Multi-Floor Indoor Propagation Analysis

Information from the abstract

This paper presents the design and experimental evaluation of a LoRa-based structural health monitoring (SHM) system for structural inclination sensing and wireless data transmission in a multi-story reinforced-concrete building. The sensing node integrates an ADXL345 three-axis accelerometer, a microcontroller, and an SX1276 LoRa transceiver operating at 923.4 MHz with a spreading factor of 7, a bandwidth of 125 kHz, and a transmit power of 14 dBm. The inclination sensing subsystem was first validated over a reference range of 0–90°, achieving a mean absolute error of 0.45° and a root mean square error of 0.48°. Wireless communication performance was then evaluated at 25 indoor measurement locations distributed across five floors using received signal strength indicator (RSSI), gateway-reported packet signal-to-noise ratio (SNR), packet delivery ratio (PDR), inter-floor attenuation, and propagation-model comparison. The measured RSSI ranged from −45 to −106 dBm, while the packet SNR ranged from 2.17 to 6.58 dB. With 100 packets transmitted at each location, the PDR ranged from 94% to 100%, demonstrating reliable packet delivery throughout the investigated environment. The average inter-floor attenuation at the 0 m horizontal reference position was 10.25 dB per adjacent-floor transition, indicating a substantial influence of vertical propagation within the reinforced-concrete structure. Propagation-model performance was evaluated using a common 20-location dataset. Among the FSPL, Log-Distance, and ITU-R P.1238 models, ITU-R P.1238 provided the closest agreement with the measured RSSI data, achieving an RMSE of 11.33 dB and an MAE of 7.21 dB. These results demonstrate the feasibility of LoRa communication for inclination-based SHM and provide practical guidance for indoor coverage assessment and gateway-placement planning in multi-story buildings.

Why this record is monitored

This record has an Impact Signal of 75/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: IoT Networks and Protocols · Structural Health Monitoring Techniques · Arduino and IoT Applications