Hardware-based online self-diagnosis for faulty device identification in large-scale IoT systems

Junghee Lee, Monobrata Debnath, Amit Patki, Mostafa Hasan, Chrysostomos Nicopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Thanks to advances in semiconductor and communication technologies, a multitude of devices can be connected over a network. This widespread interconnectivity among disparate devices has ushered the era of Internet-of-Things (IoT). After IoT devices are developed and tested, they are integrated within a system and eventually deployed. Due to the complex nature of IoT systems, however, they may fail even after deployment. In a large-scale IoT system, an automatic diagnosis technique is imperative, because it may take too much time and effort to investigate a large number of devices. In this paper, a faulty device identification technique is proposed that is based on very lightweight processor-level architectural support. A hardware-based monitoring agent is incorporated within a processor, and connected to a separate monitoring program when an examination is required. By analyzing information collected by the agent, the monitoring program determines whether the device under monitoring is working correctly, or not. The experimental results demonstrate that the proposed technique can detect 92.66% of failures, with merely 1.55% false alarms.

Original languageEnglish
Title of host publicationProceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-104
Number of pages9
ISBN (Electronic)9781538663127
DOIs
Publication statusPublished - 2018 May 25
Externally publishedYes
Event3rd ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018 - Orlando, United States
Duration: 2018 Apr 172018 Apr 20

Publication series

NameProceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018

Conference

Conference3rd ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018
CountryUnited States
CityOrlando
Period18/4/1718/4/20

Keywords

  • Control flow integrity
  • Internet of Things
  • Self test

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications

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  • Cite this

    Lee, J., Debnath, M., Patki, A., Hasan, M., & Nicopoulos, C. (2018). Hardware-based online self-diagnosis for faulty device identification in large-scale IoT systems. In Proceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018 (pp. 96-104). (Proceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IoTDI.2018.00019