Abstract
While sensor networks have been used in various applications because of the automatic sensing capability and ad-hoc organization of sensor nodes, the fault-prone characteristic of sensor networks has challenged the event detection and the anomaly detection which, to some extent, have neglected the importance of discriminating events and errors. Considering data uncertainty, in this article, we present the problem of data discrimination in fault-prone sensor networks, analyze the similarities and the differences between events and errors, and design a multi-level systematic discrimination framework. In each step, the framework filters erroneous data from the raw data and marks potential event samples for the next-step processing. The raw data set D is finally partitioned into three subsets, Devent, Derror and Dordinary. Both the scenario-based simulations and the experiments on real-sensed data are carried out. The statistical results of various discrimination metrics demonstrate high distinction ratio as well as the robustness in different cases of the network.
Highlights
One of the major applications of sensor networks is event detection [1], while the data uncertainty caused by faulty sensors increases the difficulty of distinguishing between events and errors in sensor data, and correspondingly affects the design of data processing framework in a sensor network
While sensor networks have been used in various applications because of the automatic sensing capability and ad-hoc organization of sensor nodes, the fault-prone characteristic of sensor networks has challenged the event detection and the anomaly detection which, to some extent, have neglected the importance of discriminating events and errors
Considering data uncertainty, in this article, we present the problem of data discrimination in fault-prone sensor networks, analyze the similarities and the differences between events and errors, and design a multi-level systematic discrimination framework
Summary
One of the major applications of sensor networks is event detection [1], while the data uncertainty caused by faulty sensors increases the difficulty of distinguishing between events and errors in sensor data, and correspondingly affects the design of data processing framework in a sensor network. According to the summary of the state-of-the-art anomaly detection techniques [12], there lack sufficient concern of the discrimination between events and errors in sensor data processing [3]. In this article, we focus on designing a discrimination framework to solve this problem. The performance of the framework is evaluated by two scenariobased simulations and a series of experiments on a real-world sensor dataset, and Section 5 concludes the article with some discussions on the potential extension of the framework The performance of the framework is evaluated by two scenariobased simulations and a series of experiments on a real-world sensor dataset in Section 4, and Section 5 concludes the article with some discussions on the potential extension of the framework
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