ELEC Oral Comprehensive Exam for Doctoral Candidacy by Junxing Ren - ECE Department
Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
: Zoom Link: https://umassd.zoom.us/j/96235032225 Meeting ID: 962 3503 2225 Passcode: 982487
Liudong xing
508.999.8883
lxing@umassd.edu
https://umassd.zoom.us/j/99573148168
Topic: Cascading Failure Modeling and Analysis in Functionally Dependent Internet of Things Systems
Abstract: The Internet of Things (IoT) is a complex network of components that can automatically collect, exchange, and process data, providing indispensable support for increasingly complicated and critical missions. However, functional dependencies among heterogeneous components introduce risks of fault propagation and potentially high-impact cascading failures (CFs). This research examines CF mechanisms and investigates reliability analysis methods to address the effects of CFs in two types of functionally dependent IoT systems subject to random propagation time (RPT) and cross-phase propagation (CPP), respectively. We develop separable, analytical modeling methods for assessing the mission reliability of considered IoT systems. The proposed methods employ a divide-and-conquer strategy to decompose the original reliability problem into a set of independent reduced problems that can be solved in parallel. The proposed methods are flexible in accommodating diverse types of time-to-failure distributions. Case studies on heterogeneous unmanned aerial vehicle (UAV) systems are performed to demonstrate the proposed methods and the effects of RPT and CPP on mission reliability. The application of the proposed model in supporting mission planning is demonstrated through solving reliability and redundancy allocation problems. Correctness of the proposed methods is validated using Monte Carlo simulations. This proposal also discusses future research directions in extending the proposed methods to capture more practical characteristics of IoT mission systems, developing effective mitigation strategies for enhancing system’s resilience against CFs, and artificial intelligence-powered methods for predicting CFs in UAV-based IoT systems.
Advisor: Dr. Liudong Xing, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth
Committee Members: Dr. Lance Fiondella, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Yuchou Chang, Associate Professor, Department of Computer & Information Science; Dr. David W. Coit, Professor, Department of Industrial & Systems Engineering, Rutgers University
NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public.
*For further information, please contact Dr. Liudong Xing via email at lxing@umassd.edu