TY - GEN
T1 - Parallel itinerary-based RNN query processing in location-aware WSNs
AU - Chung, Jae Hwa
AU - Jang, Hong Jun
AU - Jung, Kyung Ho
AU - Kyeong, Hur
AU - Lee, Won Gyu
AU - Jung, Soon Young
PY - 2009
Y1 - 2009
N2 - 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) = {Oi ∈ D| NN(Oi) = 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.
AB - 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) = {Oi ∈ D| NN(Oi) = 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.
KW - Itinerary
KW - Reverse nearest neighbor
KW - Sensor network
KW - Spatial query
UR - http://www.scopus.com/inward/record.url?scp=77951177010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951177010&partnerID=8YFLogxK
U2 - 10.1109/ICUT.2009.5405688
DO - 10.1109/ICUT.2009.5405688
M3 - Conference contribution
AN - SCOPUS:77951177010
SN - 9781424451302
T3 - Proceedings of the 4th International Conference on Ubiquitous Information Technologies and Applications, ICUT 2009
BT - Proceedings of the 4th International Conference on Ubiquitous Information Technologies and Applications, ICUT 2009
T2 - 4th International Conference on Ubiquitous Information Technologies and Applications, ICUT 2009
Y2 - 20 December 2009 through 22 December 2009
ER -