Data recovery in wireless sensor networks using Markov random field model

Abstract

In practical applications, because of some reasons, such as node failure or link failure, data loss is a normal occurrence in wireless sensor networks. For many applications on which data has a great impact, it is important to recover the lost data. Because of the sensory data has spatial correlation and temporal correlation, it is difficult to recover the lost data. Consider this, in this paper, we propose a novel algorithm based on the Markov Random Field to reduce the data error ratio during the data recovery process. In our proposed algorithm, we use the curved face recovery to utilize the spatial correlation in the sensory data, then we explore the temporal correlation between data in wireless sensor networks so that it can improve the accuracy of data recovered. A large amount of experiments show that, compared to other relevant works, the data recovery algorithm we proposed can reduce the error rate of the fitting data, especially data lost seriously in the wireless sensor network.