An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

With the development of sensed technology, more and more sensor nodes carry multiple sensors in information collection wireless sensor networks. As a result, there are always a large number of correlated dynamic sensing data transmitted in the network. These data contain a lot of redundant information and errors, which leads to the resource waste and causes data congestion. Although various researches have focused on the sensing data collection and fusion, most of them do not consider the correlation of sensing data, and the network cannot adaptively collect data according to the accuracy required by users. Therefore, this article proposes a hierarchical data collection scheme for data-collecting wireless sensor networks. We combine the clustering and chain network structure and propose a probabilistic multi-mode sensing data selection method based on the characteristics of the sensors. Moreover, a data correlation analysis method based on gray correlation analysis is proposed to measure the similarity of the sensing data. Furthermore, we use the Bernoulli uniform sampling to estimate the approximate average value of data quality and make the approximation for the multi-mode sensing data on the basis of required data accuracy. Experimental results show the effectiveness of the proposed approach. And experiments prove that the proposed approach has better performance than state-of-the-art approaches.