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Multi-sensor Data fusion in wireless sensor networks

Authors: Yin Zhenyu; Zhao Hai; Lin Kai; Sun Peigang; Gong Yishan; Zhang Yongqing; Xu Ye;

Multi-sensor Data fusion in wireless sensor networks

Abstract

In wireless sensor networks, the sensor nodes usually use battery power, one of the main design challenges is to obtain long system lifetime. In a sensor network, the nodes usually sense several kinds of information. These kinds of data have different characteristics. In this paper, the sensed data from the sensor nodes are divided into two types; they are emergent data and usual sensed data. And a data fusion layer is proposed. By using the data reduction algorithm, sensor nodes only transfer the emergent data and the changed information of usual sensed data to sink node. And the sink node recruits the full sensed data. Finally, we implement the data reduction algorithm in TinyOS. Experiment results show that the data reduction algorithm can save energy without losing the sensed information.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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