Belief-based cleaning in trajectory sensor streams

The imprecision in data streams received at the base station is common in mobile wireless sensor networks. The movement of sensors leads to dynamic spatio-temporal relationships among sensors and invalidates the data cleaning techniques designed for stationary networks. As one of the first methods designed for mobile environments, we introduce a novel online method to clean the imprecise or dirty data in mobile wireless sensor networks. Our method deploys a belief parameter to select the helpful neighboring sensors to clean data. The belief parameter is based on sensor trajectories and the consistency of their streaming data correctly received at the base station. The evaluation over multiple mobility models shows that the proposed method outperforms the existing data cleaning algorithms, especially in sparse environments where the node density in the system is low.