Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107834
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Type: Conference paper
Title: Cleaning environmental sensing data streams based on individual sensor reliability
Author: Zhang, Y.
Szabo, C.
Sheng, Q.
Citation: Proceedings of the 15th International Conference on Web Information Systems Engineering, Part II, 2014 / Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (ed./s), vol.8787, pp.405-414
Publisher: Springer
Issue Date: 2014
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783319117454
ISSN: 0302-9743
1611-3349
Conference Name: 15th International Conference on Web Information Systems Engineering (WISE) (12 Oct 2014 - 14 Oct 2014 : Thessaloniki, Greece)
Statement of
Responsibility: 
Yihong Zhang, Claudia Szabo and Quan Sheng
Abstract: Environmental sensing is becoming a significant way for understanding and transforming the environment, given recent technology advances in the Internet of Things (IoT). Current environmental sensing projects typically deploy commodity sensors, which are known to be unreliable and prone to produce noisy and erroneous data. Unfortunately, the accuracy of current cleaning techniques based on mean or median prediction is unsatisfactory. In this paper, we propose a cleaning method based on incrementally adjusted individual sensor reliabilities, called influence mean cleaning (IMC). By incrementally adjusting sensor reliabilities, our approach can properly discover latent sensor reliability values in a data stream, and improve reliability-weighted prediction even in a sensor network with changing conditions. The experimental results based on both synthetic and real datasets show that our approach achieves higher accuracy than the mean and median-based approaches after some initial adjustment iterations.
Keywords: Internet of Things; data stream cleaning; sensor reliability
Rights: © Springer International Publishing Switzerland 2014
RMID: 0030021103
DOI: 10.1007/978-3-319-11746-1_29
Appears in Collections:Computer Science publications

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