Redundancy Elimination of Big Sensor Data Using Bayesian Networks
نویسندگان
چکیده
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 data processing and analysis. Preprocessing is necessary for eliminating the redundancy in gathered massive sensor data. Approaches based on static Bayesian network (SBN) and dynamic Bayesian networks (DBNs) are proposed for preprocessing big sensor data, especially for redundancy elimination. In this paper, approaches based on static Bayesian network (SBN) and dynamic Bayesian networks (DBNs) 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.
منابع مشابه
Anomaly Detection and Redundancy Elimination of Big Sensor Data in Internet of Things
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 networks are considered to contain highly useful and valuable information. However, for a variety of reasons, received sensor data often appear abnormal. Therefore, effective anomaly detection methods are required to guarantee the quality of data collected...
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