SimpleTrack: adaptive trajectory compression with deterministic projection matrix for mobile sensor networks

Article

Rana, Rajib, Yang, Mingrui, Wark, Tim, Chou, Chun Tung and Hu, Wen. 2015. "SimpleTrack: adaptive trajectory compression with deterministic projection matrix for mobile sensor networks." IEEE Sensors Journal. 15 (1), pp. 365-373. https://doi.org/10.1109/JSEN.2014.2335210
Article Title

SimpleTrack: adaptive trajectory compression with deterministic projection matrix for mobile sensor networks

ERA Journal ID4437
Article CategoryArticle
AuthorsRana, Rajib (Author), Yang, Mingrui (Author), Wark, Tim (Author), Chou, Chun Tung (Author) and Hu, Wen (Author)
Journal TitleIEEE Sensors Journal
Journal Citation15 (1), pp. 365-373
Number of Pages9
Year2015
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1530-437X
1558-1748
Digital Object Identifier (DOI)https://doi.org/10.1109/JSEN.2014.2335210
Web Address (URL)http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6847688
Abstract

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using six data sets shows that our proposed algorithm can achieve submeter accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm shows that our algorithm can reduce the error by 10–60 cm for the same compression ratio.

Keywordsmobile sensor networks; trajectory compression; compressive sensing; adaptive compression; support vector regression; sparse coding; singular value decomposition
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Byline AffiliationsInstitute for Resilient Regions
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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