Intelligent Self-Adaptation Data Behavior Control Inspired by Speech Acts

Abstract

Wireless sensor networks (WSNs) are a promising technology for collecting information by utilizing various types of small-sized sensors. Implementing efficient data acquisition and transmission is important for real-time monitoring and data analysis, owing to massive and heterogeneous data with low precision. In this work, the data behavior model inspired by speech acts is built, including the data correlation model and the data comment model. Intelligent self-adaptation data behavior control is then proposed, of which the main idea is to make sensor nodes (SNs) process data intelligently. In the proposed data behavior control, the temporal compression behavior is achieved through variable-cycle transmission and the multivariate spatial compression behavior is motivated by compressed sensing (CS) theory. The multivariate spatial-temporal compression composed of the above two compression behaviors can adjust itself through data comment behaviors including the large-cycle error self-correction behavior based on the node credibility and the large-cycle event self-adaptation behavior based on the information granularity. The data behavior control presented has been validated by the experiments in the Shanghai metro tunnel. The experiment results show that these intelligent data behaviors inspired by speech acts can make WSNs more effective and intelligent with no change in the existing network structure.