Paper Info
Title
Hardware-Based Online Self-Diagnosis for Faulty Device Identification in Large-Scale IoT Systems
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.
Year
DOI
Venue
2018
10.1109/IoTDI.2018.00019
2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)
Keywords
Field
DocType
Self test,Control flow integrity,Internet of Things
Anomaly detection,Logic gate,Software deployment,Self-diagnosis,Architectural support,Computer science,Interconnectivity,Internet of Things,Computer hardware,Built-in self-test
Conference
ISBN
Citations 
PageRank 
978-1-5386-6313-4
1
0.35
References 
Authors
22
5
Name
Order
Citations
PageRank
Junghee Lee122627.26
Monobrata Debnath221.39
Amit Patki310.35
Mostafa Hasan410.35
Chrysostomos Nicopoulos583550.37