Communication-Efficient Implementation of Range-Joins in Sensor Networks

Pandit, Aditi; Gupta, Himanshu

doi:10.1007/11733836_63

Cited by 31 publications

(45 citation statements)

References 14 publications

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“…In this case, a node in the geographical centre of a triangle formed by two regions and the base-station is chosen as the processing processing node. Similarly, if the data sources are scattered throughout the network, Pandit and Gupta propose to choose a random node using the hash of the join key, as the processing node [9]. Stern et al propose Continuous Join Filtering (CJF) -a twophase approach where first summaries of static and dynamic attributes of all nodes in the network are collected, then the base-station uses these summaries to choose candidates for the join [10].…”

Section: A Related Workmentioning

confidence: 99%

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Optimal processing node discovery algorithm for distributed computing in IoT

Kolcun

Boyle

McCann

2015

2015 5th International Conference on the Internet of Things (IOT)

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Abstract-The number of Internet-connected sensing and control devices is growing. Some anticipate them to number in excess of 212 billion by 2020. Inherently, these devices generate continuous data streams, many of which need to be stored and processed. Traditional approaches, whereby all data are shipped to the cloud, may not continue to be effective as cloud infrastructure may not be able to handle myriads of data streams and their associated storage and processing needs. Using cloud infrastructure alone for data processing significantly increases latency, and contributes to unnecessary energy inefficiencies, including potentially unnecessary data transmission in constrained wireless networks, and on cloud computing facilities increasingly known to be significant consumers of energy. In this paper we present a distributed platform for wireless sensor networks which allows computation to be shifted from the cloud into the network. This reduces the traffic in the sensor network, intermediate networks, and cloud infrastructure. The platform is fully distributed, allowing every node in a homogeneous network to accept continuous queries from a user, find all nodes satisfying the user's query, find an optimal node (Fermat-Weber point) in the network upon which to process the query, and provide the result to the user. Our results show that the number of required messages can be decreased up to 49% and processing latency by 42% in comparison with state-of-the-art approaches, including Innet.

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Section: A Related Workmentioning

confidence: 99%

“…An alternative is processing on a random node in the network. The node could be chosen using, for example, a hash function [8], [9]. However, it has been shown that processing data on a random node leads to higher network traffic than processing at-the-base [11].…”

Section: Evaluation a Setupmentioning

confidence: 99%

Optimal processing node discovery algorithm for distributed computing in IoT

Kolcun

Boyle

McCann

2015

2015 5th International Conference on the Internet of Things (IOT)

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“…[14] processes the join on the path between the input data and the query issuer. [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.…”

Section: Related Workmentioning

confidence: 99%

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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“…Differently from us, they do not investigate the allocation of memory at the nodes in the join region and the synchronized data flow. Pandit and Gupta [11] propose two algorithms for in-network processing of the range-join operator. Both works [4,11] consider that the optimal join location is the weighted centroid of the triangle ABO.…”

Section: Related Workmentioning

confidence: 99%

“…Bonfils and Bonnet [3] investigate placing a correlation operator at a node in the network. Pandit and Gupta [11] propose two algorithms for processing a range-join operator in the network and Yu at al. [16] propose an algorithm for processing equi-joins.…”

Section: Introductionmentioning

confidence: 99%