Power Saving Method using Compressed Sensing Technique for IoT-based Time-series Environment Monitoring System

This paper proposes a power-saving method for time series environment monitoring wireless sensor networks (WSNs) system using the compressed sensing technique. The data reconstruction of compressed sensing is complex, but the compression process itself is simple. While sensor nodes have limited resources, servers have abundant resources. Therefore, the sensor nodes compress the environmental data measured in time series, and the server reconstructs the compressed environmental data. In the environmental data collection phase, the sensor nodes transmit environmental measurement data using compressed sensing to save energy at the sensor nodes. The server reconstructs the received environmental data. This study develops the ZigBee WSN based time series indoor environment data collection system. Then we investigate the impact of compressed sensing technology on WSNs. The experimental results show that for sensors with dynamically varying sleep periods, when the compression ratio is set to 20% or less, the power consumption is reduced by 20% with a decision coefficient of 0.7 or higher, confirming the effectiveness of the proposed method.