Similarity Based Optimization for Multiple Query Processing in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been proposed for a large variety of applications. As the number of applications of sensor networks continue to grow, the number of users in sensor networks increases as well. Consequently, it is not uncommon that base station often needs to process multiple queries simultaneously. Furthermore, these queries often need to collect data from some particular sets of sensors such as the sensors in a hot spot. To reduce the communication cost of multiple query processing in WSNs, this paper proposes a new optimization technique based on similarities among multiple queries. Given a set of queries, Q , the proposed scheme constructs a set of shared intermediate views (SIVs) from Q . Each SIV identifies a set of shared data among queries in Q . The SIVs, are processed only once, but reused by at least two queries in Q . The queries in Q , are rewritten into a different set of queries, Q ***. The collected sensor data from Q *** and SIVs, are aggregated and returned as the processing results for the original set of queries in Q . The simulation results show that the proposed technique can effectively reduce the communication cost of multiple query processing in WSNs.

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