TY - GEN
T1 - Hardware-based online self-diagnosis for faulty device identification in large-scale IoT systems
AU - Lee, Junghee
AU - Debnath, Monobrata
AU - Patki, Amit
AU - Hasan, Mostafa
AU - Nicopoulos, Chrysostomos
N1 - Funding Information:
This paper is partially supported by the LMI Research Institute. The authors would like to acknowledge the generous support of the LMI Research Institute’s Academic Partnership Program. The authors would also like to thank the members of LMI’s Bill Crowder and Virginia Stouffer for their guidance and expertise throughout the duration of this project.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - 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.
AB - 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.
KW - Control flow integrity
KW - Internet of Things
KW - Self test
UR - http://www.scopus.com/inward/record.url?scp=85048466157&partnerID=8YFLogxK
U2 - 10.1109/IoTDI.2018.00019
DO - 10.1109/IoTDI.2018.00019
M3 - Conference contribution
AN - SCOPUS:85048466157
T3 - Proceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018
SP - 96
EP - 104
BT - Proceedings - ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2018
Y2 - 17 April 2018 through 20 April 2018
ER -