Asynchronous Sampling of Correlated Data in Wireless Sensor Networks

In this paper, the authors explore a novel method based on asynchronous sampling in order to reduce the data redundancy among spatially correlated nodes in wireless sensor networks. The authors show that when the sensor nodes sample the interested field at different time points, the correlation among the sensory data can be effectively reduced and hence the sampling rate can be decreased while maintaining desired reconstruction precision. The method is different from the approaches working in a synchronized fashion and implementing compression algorithm for redundancy reduction. Our key idea is to separate the signal into a two parts. One is the common part among the sensor nodes. The other is the innovation part, which represents the distinctions among the sensor nodes. The correlation resulted from the common part can be effectively reduced through asynchronous sampling. Furthermore, the authors design reconstruction method to recover the signal from the asynchronous samples, even with imperfect clock synchronization and irregular samples. In this sampling based approach, a sensor node is not required to perform any additional function such as compression or information exchange. Rather, the extensive work of reconstruction is performed at the sink, which is usually with abundant resources. This asymmetric operation is in particular suitable for resource-constraint wireless sensor networks.