University Events

Description: Topic: Dataset Generation for Deep Learning to Authenticate Wireless Sensor Network (WSN) at Physical Layer for Structural Health Monitoring (SHM) of Transportation Infrastructure

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

Zoom Conference Link: https://umassd.zoom.us/j/93281343753
Meeting ID: 932 8134 3753 Passcode: 518247

Abstract:
A wireless sensor network (WSN) for structural health monitoring (SHM) is a network with autonomous, spatially distributed sensor nodes that communicate wirelessly in a cooperative way to monitor physical or environmental conditions. WSN for SHM has garnered interest for protecting transportation infrastructure for the safe operation and maintenance of bridges due to their ability to collect real-time data. Two concerns that arise when designing and deploying these systems are energy consumption and information security. Limited battery capacity on sensor nodes, especially on bridges, can significantly shorten WSN's lifetime. WSNs are left vulnerable to attacks on data integrity, confidentiality and availability from malicious actors masquerading as sensor nodes. This thesis proposes a scheme to protect data transmissions in WSNs for SHM without sacrificing energy consumption. The scheme solves these problems by combining state-of-the-art technologies in deep learning, radio frequency (RF) fingerprinting and RF energy harvesting. RF Fingerprinting leverages process imperfections in transceivers that can be used in a deep neural network to authenticate known sensor nodes. Deep learning is also less computationally intensive than more common forms of data security like encryption and decryption. RF energy harvesting harnesses electromagnetic waves to convert to electrical energy that powers sensor nodes wirelessly. Deep learning requires a dataset to train the model and each device needs its own dataset generation just like collecting fingerprints to establish a directory. This unique feature due to WSN for SHM of transportation infrastructure calls for the need for a framework to systematically generate datasets from individual sensor nodes. This brings out a novel approach of common applications in deep learning. The work shown acts as a proof of concept for this framework of data generation by building a prototype to present its feasibility through experimentation with using RF energy harvesting. This work also provides a framework for generating a dataset of device RF fingerprint to be used in a deep learning network to authenticate each sensor node.

Co-Advisor(s):
Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

Committee Members:
Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

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. Hong Liu via email at hliu@umassd.edu