NDLI: Data Preservation in Data-Intensive Sensor Networks With Spatial Correlation

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Data Preservation in Data-Intensive Sensor Networks With Spatial Correlation

Content Provider	ACM Digital Library
Author	Tang, Bin Crary, Nathaniel Taase, Setu
Abstract	Many data-intensive sensor network applications are potential big-data enabler: they are deployed in challenging environments to collect large volume of data for a long period of time. However, in the challenging environments, it is not possible to deploy base stations in or near the sensor field to collect sensory data. Therefore, the overflow data of the source nodes is first offloaded to other nodes inside the network, and is then collected when uploading opportunities become available. We call this process data preservation in sensor networks. In this paper, we take into account spatial correlation that exist in sensory data, and study how to minimize the total energy consumption in data preservation. We call this problem data preservation problem with data correlation. We show that with proper transformation, this problem is equivalent to minimum cost flow problem, which can be solved optimally and efficiently. Via simulations, we show that it outperforms an efficient greedy algorithm.
Starting Page	7
Ending Page	12
Page Count	6
File Format	PDF
ISBN	9781450335249
DOI	10.1145/2757384.2757389
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Energy-efficiency Spatial correlation Sensor networks Big data Data preservation
Content Type	Text
Resource Type	Article

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