Energy-Efficient Data Gathering in Wireless Sensor Network Using Compressive Sensing
Wireless Sensor Network (WSN) is wildly used for a range of applications, one of the most important issues is to improve network lifetime of the sensor node powered by battery. Inspired by compressive sensing theory, the authors proposed an energy-balanced scheme of data gathering denoted by Changeable Probability Compressive Sensing (CPCS). In this paper, they use Compressive Sensing (CS) to reduce the transmission costs. Moreover, they design a method for sensors to dynamically adjust their probability involved in every CS measurement according to the received average in-network residual energy. With extensive experiments over real sensory data, they demonstrate that the proposed method can effectively prolong the network lifetime.