Compressive Sensing-Based Data Acquisition and Reconstruction Framework for Energy-Efficient Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are an essential component in environmental monitoring, industrial automation, healthcare and Internet of Things (IoT) applications. The small energy budget of sensor nodes, however, and the expense of data transmission are high risks for network life and efficiency. To solve these problems, this study suggests acquiring and reconstructing data by using the compressive sensing method for EWSN. The proposed framework utilizes the sparsity property of a sensed signal to sample and send compressed measurements instead of the entire signal, which both saves energy and communication burden. On the receiver end, a sparse recovery algorithm is used to correctly reconstruct the original signal from the compressed measurements. The framework consists four stages: signal acquisition, sparse representation, compressive sampling, and reconstruction. The proposed method was experimentally evaluated in a simulated wireless sensor network setup of varying sensing and transmission conditions. Compression Ratio (CR), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Energy Consumption, and Reconstruction Time were used to evaluate the effectiveness of the proposed approach. The results obtained show that the presented framework proves that a substantial reduction in transmitted data has been achieved while retaining high reconstruction accuracy and fidelity of the transmitted signal. In addition, the network lifetime was extended due to the significant energy saving obtained with respect to the traditional acquisition methods. Results show that compressive sensing is an effective and reliable approach to data collection and transmission in wireless sensor networks (WSN) under an energy constraint, and can be employed in a large scale and resource-limited sensing application scenario.