Publication Type

Conference Proceeding Article

Publication Date

9-2014

Abstract

We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and compare it with traditional methods for temporal and spatial data compression. The experimental validation reveals that our approach outperforms several existing wireless sensor network's data compression methods in terms of compression efficiency and signal reconstruction.

Discipline

OS and Networks

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

First Page

307

Last Page

311

ISBN

9781450330305

Identifier

10.1145/2641798.2641799

Publisher

ACM

City or Country

Montreal, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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