Power-Efficient Wireless Sensor Network Using Distributed Compressed Sensing for Time-Series Environmental Monitoring

—Understanding environmental conditions in different locations is crucial for addressing air-pollution issues. While wireless sensor networks offer the capability to monitor environmental quality locally, they face challenges related to power supply. This study introduces a low-power Wireless Sensor Network (WSN) employing distributed compressed sensing for a time-series environmental monitoring system. The proposed method achieves data compression at individual sensor nodes, mitigating power consumption during data transmission. Conversely, data restoration occurs on a server equipped with ample computing resources. This study investigates the power-saving impact of the proposed approach and identifies the optimal compression ratio. Experimental findings reveal a coefficient of determination of 0.9 or higher at a compression ratio of 90%. Our results indicate that the distributed compressed sensing-based WSN proposed in this study is effective for time-series environmental monitoring systems, offering valuable insights for future research endeavors.