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Open AccessArticle

Approximate Sensory Data Collection: A Survey

1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
2
Department of Computer Science, Georgia State University, Atlanta, 30303, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Dongkyun Kim, Houbing Song, Juan-Carlos Cano, Wei Wang, Waleed Ejaz and Qinghe Du
Sensors 2017, 17(3), 564; https://doi.org/10.3390/s17030564
Received: 14 December 2016 / Revised: 14 February 2017 / Accepted: 6 March 2017 / Published: 10 March 2017
With the rapid development of the Internet of Things (IoTs), wireless sensor networks (WSNs) and related techniques, the amount of sensory data manifests an explosive growth. In some applications of IoTs and WSNs, the size of sensory data has already exceeded several petabytes annually, which brings too many troubles and challenges for the data collection, which is a primary operation in IoTs and WSNs. Since the exact data collection is not affordable for many WSN and IoT systems due to the limitations on bandwidth and energy, many approximate data collection algorithms have been proposed in the last decade. This survey reviews the state of the art of approximatedatacollectionalgorithms. Weclassifythemintothreecategories: themodel-basedones, the compressive sensing based ones, and the query-driven ones. For each category of algorithms, the advantages and disadvantages are elaborated, some challenges and unsolved problems are pointed out, and the research prospects are forecasted. View Full-Text
Keywords: approximate computation; sensory data collection; internet of things; wireless sensor networks approximate computation; sensory data collection; internet of things; wireless sensor networks
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Cheng, S.; Cai, Z.; Li, J. Approximate Sensory Data Collection: A Survey. Sensors 2017, 17, 564.

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