Energy-Efficient Distributed Spatial Join Processing in Wireless Sensor Networks

Min Soo KIM; Jin Hyun SON; Ju Wan KIM; Myoung Ho KIM

doi:10.1587/transinf.E93.D.1447

Energy-Efficient Distributed Spatial Join Processing in Wireless Sensor Networks

Min Soo KIM, Jin Hyun SON, Ju Wan KIM, Myoung Ho KIM

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In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.6 pp.1447-1458

Publication Date: 2010/06/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.1447

Type of Manuscript: Special Section PAPER (Special Section on Info-Plosion)

Category: Spatial Databases

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Min Soo KIM, Jin Hyun SON, Ju Wan KIM, Myoung Ho KIM, "Energy-Efficient Distributed Spatial Join Processing in Wireless Sensor Networks" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 6, pp. 1447-1458, June 2010, doi: 10.1587/transinf.E93.D.1447.
Abstract: In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1447/_p

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@ARTICLE{e93-d_6_1447,
author={Min Soo KIM, Jin Hyun SON, Ju Wan KIM, Myoung Ho KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Energy-Efficient Distributed Spatial Join Processing in Wireless Sensor Networks},
year={2010},
volume={E93-D},
number={6},
pages={1447-1458},
abstract={In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.},
keywords={},
doi={10.1587/transinf.E93.D.1447},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Energy-Efficient Distributed Spatial Join Processing in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1447
EP - 1458
AU - Min Soo KIM
AU - Jin Hyun SON
AU - Ju Wan KIM
AU - Myoung Ho KIM
PY - 2010
DO - 10.1587/transinf.E93.D.1447
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2010
AB - In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.
ER -