Data reconstruction with spatial and temporal correlation in wireless sensor networks

Abstract

Data loss is a common phenomenon in the practical wireless sensor networks due to several observations, such as the node or link failure, the expensive cost of re-transmission, and so on. How to reconstruct the lost data is one important issue for many applications which concern heavily on the data gathering issues. Most of the current data reconstruction solutions are based on the spatial correlation and they adopt interpolation methods to approximate the data reconstruction process, which leads to bad performance especially in case that the number of data loss increases in the network. In this paper, we consider the spatial and temporal correlation simultaneously, and propose a novel data reconstruction algorithm to improve the data error ratio in the wireless sensor networks. The proposed algorithm can utilize the spatial correlation by using the curved face reconstruction, and it is also helpful to improve the accuracy of data reconstruction by exploring the temporal correlation among the sensory data. Extensive experiments demonstrate that the proposed data reconstruction algorithm can significantly reduce the fitting data error ratio compared with related works especially in case that data loss is serious in the network.