HIPaG: An energy-efficient in-network join for distributed condition tables in sensor networks

Jeon, Joo Hyuk; Lee, Ki Yong; Yoo, Jae Soo; Kim, Myoung Ho

doi:10.1016/j.jss.2009.01.012

Cited by 1 publication

(2 citation statements)

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“…REED shows significant energy savings through the in-network join, but it does not work well when the table is too large or the density of a sensor network is not sufficiently high. For an extension of REED, Jeon et al [24] proposed HIPaG, called the Hybrid In-network join with join Paths and join Groups. Here, a join group is the same as a sensor group in REED.…”

Section: Related Workmentioning

confidence: 99%

“…They insist that their perpendicular approach should be load-balanced, communicationefficient, and should incur near-optimal transmission cost for the special case of binary joins in grid networks. Although these algorithms [10], [16], [23], [24] show good performance through the in-network join, they cannot be directly applied to our problem in the following reasons. First, the algorithms do not present a solution for spatial join processing.…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Distributed Spatial Joint Processing in Wireless Sensor Networks

Kim¹,

Son

Kim³

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn 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 naïve and existing approaches through extensive experiments and clarify our approach's distinguished features. key words: distributed spatial index, distributed spatial join, in-network query processing, wireless sensor network

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%