HIBOR: An efficient approach to sequence pattern query processing in wireless sensor networks

Sequence-based query processing has not attracted much attention in wireless sensor networks though its counterpart has been studied extensively in time series databases. So as to answer such queries of interest, data distribution collected by sensor nodes and moving trends of sequences can be captured by HIBOR (Histogram with Bit vectOR). We consider the problem of distributed clustering and querying over histograms with bit vectors of moving trends of sensor data sequences. Especially, we are interested in efficiently answering the following query, namely query by example: return all the sensor nodes that have observed a particular sequence pattern issued by the user with specified thresholds. In this paper, we present a novel approach to addressing the query mentioned above efficiently. First, the whole sensor network is partitioned into several clusters. Second, a distributed index is built on the clustering result, which is based on average histograms and bit vectors. Using hierarchical histograms maintained at different layers, HIBOR can prune as many branches as possible during query processing. Extensive experiments on both real-world and synthetic data sets show that HIBOR significantly reduces total communication overheads and extends network lifetime.