Precise Correlation Extraction for IoT Fault Detection with Concurrent Activities

Gyeongmin Lee, Bongjun Kim, Seungbin Song, Changsu Kim, Jong Kim, Hanjun Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number94
JournalACM Transactions on Embedded Computing Systems
Volume20
Issue number5s
DOIs
Publication statusPublished - 2021 Oct

Bibliographical note

Funding Information:
This article appears as part of the ESWEEK-TECS special issue and was presented in the International Conference on Embedded Software (EMSOFT), 2021. This work is supported by IITP-2020-0-01847, IITP-2020-0-01361 and IITP-2021-0-00853 through the Institute of Information and Communication Technology Planning and Evaluation (IITP) funded by the Ministry of Science and ICT. This work is also supported by Samsung Electronics. Authors’ addresses: G. Lee, Samsung Advanced Institute of Technology, 130 Samsung-ro Yeongtong-gu, Suwon, Gyeonggi, 16678, Republic of Korea; email: gm05.lee@samsung.com; B. Kim, C. Kim, and J. Kim, POSTECH, 77 Cheongam-Ro Nam-Gu, Pohang, Gyeongbuk, 37673, Republic of Korea; emails: {bong90, kcs9301, jkim}@postech.ac.kr; S. Song and H. Kim (corresponding author), Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea; emails: {seungbin, hanjun}@yonsei.ac.kr. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 1539-9087/2021/09-ART94 $15.00 https://doi.org/10.1145/3477025

Publisher Copyright:
© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture

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