Journal article
Efficient Compression Algorithm with Limited Resource for Continuous Surveillance
KSII transactions on Internet and information systems, v 10(11), pp 5476-5496
30 Nov 2016
Abstract
Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.
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Details
- Title
- Efficient Compression Algorithm with Limited Resource for Continuous Surveillance
- Creators
- Ling Yin - South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R ChinaChuanren Liu - Drexel Univ, Dept Decis Sci & Management Informat Syst, Philadelphia, PA 19104 USAXinjiang Lu - Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R ChinaJiafeng Chen - South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R ChinaCaixing Liu - South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China
- Publication Details
- KSII transactions on Internet and information systems, v 10(11), pp 5476-5496
- Publisher
- Ksii-Kor Soc Internet Information
- Number of pages
- 21
- Grant note
- 201408440035 / China Scholarship Council
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Microbiology and Immunology
- Web of Science ID
- WOS:000394310100015
- Scopus ID
- 2-s2.0-85006056545
- Other Identifier
- 991019173418304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Telecommunications