Parallel itinerary-based RNN query processing in location-aware WSNs

The Reverse Nearest Neighbor (RNN) query is to find the objects in objects dataset D that have Q closer to them than any other object in D. Formally RNN(Q) = {O_i ∈ D| NN(O_i) = Q}. Owing to technical advances of sensor and wireless techniques, sensor nodes are deployed over a wide range and applied to various applications with the RNN query. To date, centralized and in-network scheme based RNN query processing approaches have been researched. However, these approaches collect data from sensors regardless of query issuing and inevitably deplete energy and CPU capacity. Therefore, in this paper, we propose the parallel itinerary-based RNN (PIRNN) query processing algorithm. The PIRNN algorithm does not rely on any centralized or in-network data collection scheme. Moreover, PIRNN disseminates multiple itineraries concurrently and restricts the search range to decrease query latency. In order to support the performance of PIRNN algorithm, we revise two representative RNN processing methods, SAA and HP, used in mobile networks. The extensive simulation results prove that the PIRNN method yields better performance and less energy consumption over the conventional one.