Energy efficient data gathering in wireless sensor networks and internet of things with compressive sensing at sensor node

Compressive sensing is the emerging theory in the field of wireless sensor networks which works on a Sub-Nyquist sampling theorem. Sparse representation of a few non-zero samples of the original signal will significantly reduce the number of samples. Also, reconstruction of the original signal is possible as per Sub-Nyquist sampling theorem. Internet of Things (IoT) has become an immensely popular field in wireless communication. The system an Internet of Things can be formed by thousands of independent components e.g. RFID tags, sensors, mobile phones etc. Compressive sensing theory provides a promising approach in the field of wireless sensor network. This paper investigates how compressive sensing can be applicable in an Internet of Things for energy efficiency with low computational cost. The compressive sensing based framework is proposed for Internet of Things and wireless sensor network aiming to increase the performance of a system with energy efficiency at sensor node.