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Data aggregation technique combined temporal-spatial correlation with compressed sensing in wireless sensor networks

In wireless sensor networks (WSNs), energy-efficient data gathering and low-cost data transmission is very important for application, due to significant power constraints on the sensors. Our goal is to exploit temporal-spatial correlation and minimize the number of the required samples, reducing the cost of energy. We propose a data aggregation technique combined temporal-spatial correlation with compressed sensing (CS), where routing is used in conjunction with CS. In particular, we present an Iterative Hard Thresholding (IHT) algorithm based on temporal-spatial correlation. We then evaluate the performance of our proposed algorithm using synthetic signal. The results show that we can achieve significant savings in the total number of the required samples compared to the traditional CS schemes.