Kavi Khedo, Rubeena Doomun, Sonum Aucharuz
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DOI: 10.4236/wsn.2010.24041   PDF    HTML     6,329 Downloads   13,274 Views   Citations

Abstract

In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energy will be wasted and thus the network energy will be depleted quickly. Data aggregation is an important paradigm for compressing data so that the energy of the network is spent efficiently. In this paper, a novel data aggregation algorithm called Redundancy Elimination for Accurate Data Aggregation (READA) has been proposed. By exploiting the range of spatial correlations of data in the network, READA applies a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without largely losing the accuracy of the final aggregated data. One peculiarity of READA is that it uses a prediction model derived from cached values to confirm whether any outlier is actually an event which has occurred. From the various simulations conducted, it was observed that in READA the accuracy of data has been highly preserved taking into consideration the energy dissipated for aggregating the data

Keywords

Sensor Networks, Data Aggregation, Data Compression, Event Detection, Redundancy Elimination

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Conflicts of Interest

The authors declare no conflicts of interest.

References

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