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Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities SCIE SCOPUS

Title
Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities
Authors
Lee, GyeongminKim, BongjunSong, SeungbinKim, ChangsuKim, JongKim, Hanjun
Date Issued
2021-10
Publisher
ASSOC COMPUTING MACHINERY
Abstract
In the Internet of Things (IoT) environment, detecting a faulty device is crucial to guarantee the reliable execution of IoT services. To detect a faulty device, existing schemes trace a series of events among IoT devices within a certain time window, extract correlations among them, and find a faulty device that violates the correlations. However, if a few users share the same IoT environment, since their concurrent activities make non-correlated devices react together in the same time window, the existing schemes fail to detect a faulty device without differentiating the concurrent activities. To correctly detect a faulty device in the multiple concurrent activities, this work proposes a new precise correlation extraction scheme, called PCoExtractor. Instead of using a time window, PCoExtractor continuously traces the events, removes unrelated device statuses that inconsistently react for the same activity, and constructs fine-grained correlations. Moreover, to increase the detection precision, this work newly defines a fine-grained correlation representation that reflects not only sensor values and functionalities of actuators but also their transitions and program states such as contexts. Compared to existing schemes, PCoExtractor detects and identifies 40.06% more faults for 4 IoT services with concurrent activities of 12 users while reducing 80.3% of detection and identification times.
URI
https://oasis.postech.ac.kr/handle/2014.oak/110096
DOI
10.1145/3477025
ISSN
1539-9087
Article Type
Article
Citation
ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, vol. 20, no. 5, 2021-10
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김종KIM, JONG
Dept of Computer Science & Enginrg
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