Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average.
However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.