Abstract
In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor network are considered to contain highly useful and valuable information. However, since sensor data are usually correlated in time and space, not all the gathered data are valuable for further processing and analysis. Preprocessing is necessary for eliminating the redundancy in gathered massive sensor data. In this paper, approaches based on static Bayesian network (SBN) and dynamic Bayesian network (DBN) are proposed for preprocessing big sensor data, especially for redundancy elimination. Static sensor data redundancy detection algorithm (SSDRDA) for eliminating redundant data in static data sets and real-time sensor data redundancy detection algorithm (RSDRDA) for eliminating redundant sensor data in real-time are proposed. Experimental results show that the proposed algorithms are feasible and effective.
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Acknowledgment
This work is supported by the Fundamental Research Funds for the Central Universities (N140404015, N150402004, and N140404013) and the National Natural Science Foundation of China (61271350).
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Xie, S., Chen, Z., Fu, C., Li, F. (2016). Redundancy Elimination of Big Sensor Data Using Bayesian Networks. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_16
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DOI: https://doi.org/10.1007/978-3-319-42553-5_16
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