A Priority-aware Multidimensional Top-k Query Processing in Wireless Sensor Networks

Lately the advances in wireless sensor networks show a trend towards supporting preference top-k query operator, defined as a linear combination of multi-attribute values from multi-attribute sensors with different sensing capabilities. This operator enables users to achieve only a small set of data with the highest scores. A challenging problem is to support the preference top-k query for multi-attribute sensors in skewness distribution. In this paper, we consider the problem of processing a large number of continuous multidimensional top-k queries, each with its own preference, in the skewness distribution of multi-attribute sensor nodes. Towards this goal, we explore a priority-aware index tree to support the efficient filtering through querying branch nodes according to their top-k result generation probabilities. Our approach is adaptive enough to incorporate some advanced features; for instance, we show how branch node priority can be applied. In order to further eliminate the transmission of invalid thresholds and query results, we develop an enhanced top-k query solution based on dual transform and K-skyband. Through simulations we demonstrate the efficiency of our approach compared with other reactive methods.