Abstract
A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.
Highlights
Wireless sensor networks (WSNs), typically consisting of a vast number of densely deployed and collaborative batterypowered sensors, have been widely used in various application fields, such as the environment, industry, and the military [1]
In the stationary sink scenario, since the observed data should be transmitted to the sink by multihop forwarding transmission, the high transmission energy consumption by the sensors is a problem that must be considered, which depends on the routing model and the data reduction technique
To solve the above problems and further improve the energy efficiency, we propose an energy efficient data gathering scheme exploiting spatial-temporal correlation for the WSNs
Summary
Wireless sensor networks (WSNs), typically consisting of a vast number of densely deployed and collaborative batterypowered sensors, have been widely used in various application fields, such as the environment, industry, and the military [1]. There are two main types of data gathering methods for WSNs: the mobile sink based data gathering methods [2] and the stationary sink based data gathering methods [3]. In the stationary sink scenario, since the observed data should be transmitted to the sink by multihop forwarding transmission, the high transmission energy consumption by the sensors is a problem that must be considered, which depends on the routing model and the data reduction technique. The high energy efficient data collection with efficient routing is the key in the stationary sink scenario. Compressed sensing [16], as an advanced sampling theory, provides a new data compression solution, and it indicates that only a small fraction of data projections is needed to reconstruct all of the raw data, which contains many zero entries. The M-dimension vector z can be used to recover the raw data x by solving a l1-norm minimization
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