On Join Location in Sensor Networks

Coman, Alexandru; Nascimento, Mário A.

doi:10.1109/mdm.2007.35

Cited by 23 publications

(56 citation statements)

References 7 publications

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“…Other proposals deal with energy-efficient methods to reduce energy consumption during query processing [4][5][6]. The general strategies for join query processing in sensor networks can be classified depending on the location of the join region in the sensor network [7]. The main problem with these general approaches is the communication cost overhead associated with low join selectivity.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Processing of Join Queries for Sensor Networks Using Column-Oriented Databases

Kim

2013

International Journal of Distributed Sensor Networks

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Recently, the sensor network area is gaining attention both in the industry and academia. Many applications of sensor network such as vehicle tracking and environmental monitoring require joining sensor data scattered over the network. The main performance criterion for queries in a sensor network is to minimize the battery power consumption in each sensor node. Hence, reducing the communication cost of shipping data among sensor nodes is important since it is the main consumer of battery power. In this paper, we propose a technique for join queries in a sensor network that minimizes communication cost. For storage of sensor data, we use a column-oriented database that stores data on disk (or in memory) column-by-column unlike traditional database that store data in rows. The justification for using a column-oriented database technique is not to ship those data columns that do not participate in the actual join. We compare our algorithm with existing join algorithms for sensor networks that are based on traditional roworiented databases. The performance analysis show that our proposed algorithm based on column-oriented databases outperforms existing algorithms in processing binary equi-join (BEJ) queries for sensor networks.

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Section: Related Workmentioning

confidence: 99%

An Efficient Processing of Join Queries for Sensor Networks Using Column-Oriented Databases

Kim

2013

International Journal of Distributed Sensor Networks

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“…[15] extends the approach for range queries. Coman et al [8] present (among other methods) a "mediated join" which computes the result at a central location inside the network. [16] refines this join variant by saying how to handle the data flow in order to meet the memory constraints of the nodes.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, there are methods that restrict the placement of the tuples involved. For instance, some require the tuples to be located in small regions that are close to each other, e.g., [7], [8], [9] (cf. Section II).…”

Section: Introductionmentioning

confidence: 99%

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Towards Efficient Processing of General-Purpose Joins in Sensor Networks

Stern

Buchmann

Böhm

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-Join processing in wireless sensor networks is difficult: As the tuples can be arbitrarily distributed within the network, matching pairs of tuples is communication intensive and costly in terms of energy. Current solutions only work well with specific placements of the nodes and/or make restrictive assumptions. In this paper, we present SENS-Join, an efficient general-purpose join method for sensor networks. To obtain efficiency, SENS-Join does not ship tuples that do not join, based on a filtering step. Our main contribution is the design of this filtering step which is highly efficient in order not to exhaust the potential savings. We demonstrate the performance of SENS-Join experimentally: The overall energy consumption can be reduced by more than 80%, as compared to the state-of-the-art approach. The per node energy consumption of the most loaded nodes can be reduced by more than an order of magnitude.

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“…Bonfils et al [19] study a query that correlates the sensors' measurements obtained from two pre-defined spatial regions, say R 1 and R 2 . Coman et al [20] developed a cost model to determine the most promising sensor location for performing the join with low communication cost. In a similar problem setting, Yu et al [12] propose an in-network synopsis join strategy to prune unqualified tuples that cannot contribute to join results in the initial phase of join processing.…”

Section: B In-network Operators and Filtersmentioning

confidence: 99%

Thresholded Range Aggregation in Sensor Networks

Lin

Yiu

Mamoulis

2009

2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware

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Abstract-The recent advances in wireless sensor technologies (e.g., Mica, Telos motes) enable the economic deployment of lightweight sensors for capturing data from their surrounding environment, serving various monitoring tasks, like forest wildfire alarming and volcano activity. We propose a novel query called thresholded range aggregate query (TRA), which retrieves the IDs of the sensors for which the average measurement in their neighborhood exceeds a user-given threshold. This query provides results that they are robust against individual sensor abnormality, and yet precisely summarize the sensors' status in each local region. In order to process the (snapshot) TRA query, we develop energy-efficient protocols based on appropriate operators and filters in sensor nodes. The design of these operators and filters is non-trivial, due to the fact that each sensor measurement influences the actual results of other nodes in its neighborhood region. Furthermore, we extend our protocols for continuous evaluation of the TRA query. Experimental results show that our proposed solutions indeed offer substantial energy savings for both real and synthetic sensor networks.

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On Join Location in Sensor Networks

Cited by 23 publications

References 7 publications

An Efficient Processing of Join Queries for Sensor Networks Using Column-Oriented Databases

An Efficient Processing of Join Queries for Sensor Networks Using Column-Oriented Databases

Towards Efficient Processing of General-Purpose Joins in Sensor Networks

Thresholded Range Aggregation in Sensor Networks

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