STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks
Data gathering in sensor networks is required to be efficient, adaptable and robust. Recently, Compressive Sensing (CS) based data gathering shows promise in meeting these requirements. Existing CS-based data gathering solutions require that a transform that best sparsifies the sensor readings should be used in order to reduce the amount of data traffic in the network as much as possible. As a result, it is very likely that different transforms have to be determined for varied sensor networks, which seriously affects the adaptability of CS-based schemes. In addition, the existing schemes result in significant errors when the sampling rate of sensor data is low (equivalent to the case of high packet loss rate) because CS inherently requires the number of measurements should exceed a certain threshold.