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Thursday, April 25, 2024
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1:00 PM
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4/27
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CPE Master of Science Thesis Defense by Christopher Dentremont - ECE Department
- Location: > See description for location
- Cost: Free
- Contact: > See Description for contact information
- 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
- Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering
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2:30 PM
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4:30 PM
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CPE Master of Science Thesis Defense by Bryce Afonso - ECE Department
- Location: > See description for location
- Cost: Free
- Contact: > See Description for contact information
- Description: Topic: Network-less Wireless Sensing for Structural Health Monitoring (SHM) of Bridges: Unmanned Aerial Vehicle (UAV) Investigations
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:
The Internet of Things (IoT) has significantly advanced the application of Wireless Sensor Networks (WSNs) in Structural Health Monitoring (SHM), particularly for civil engineering infrastructure such as bridges. Despite the advancements, the widespread application of WSNs in SHM remains hindered by their limited network lifetime, posing a significant hurdle to their adoption. Furthermore, IoT and WSNs open a new attack surface. Designing SHM systems with wireless sensors utilizing no network allows system resiliency to cyber-attacks. Unmanned Aerial Vehicles (UAVs) have been heralded for their potential to overcome these limitations through secure and efficient data collection. This thesis expands on the existing UAV application by proposing a novel UAV-assisted WSN system that employs Bluetooth Low Energy (BLE) as the communication protocol for synchronized data gathering in SHM systems. Our design diverges from traditional multi-hop WSNs by leveraging UAVs as mobile data sinks, reducing the energy burden on individual sensor nodes, and significantly prolonging the sensor's operational life. Through an analytical study, we demonstrate that our UAV-BLE system offers a remarkable improvement in network lifetime in comparison to conventional network routed WSNs. Additionally, the use of BLE facilitates a lightweight authentication scheme, providing secure wireless communication between sensor nodes and the UAV. Thus, this novel approach enhances the overall robustness and longevity of SHM systems. A proof-of-concept implementation utilizing a PASCO bridge kit equipped with wireless load cell sensors, demonstrates the feasibility of our approach. To the best of the authors' knowledge, this is the first exploration of a BLE-centric synchronization scheme in the context of SHM, marking a significant leap toward secure, safe, reliable, and efficient monitoring of civil engineering structures.
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;
Dr. Tzuyang Yu, Professor, Department of Civil and Environmental Engineering, UMASS Lowell
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
- Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering
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7:00 PM
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9:00 PM
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Italian Studies Film Series - Once Upon a TIme in the West (1968)
- Location: > See description for location
- Contact: > See Description for contact information
- Description: Italian Studies invites you to enjoy a year of Spaghetti Westerns. Starting in the 1960s Italian directors began to apply their own artistic approach and their own political and social concerns to the old-fashioned western genre. The result? Some of the most artistically exciting movies of the 1960s and 1970s.
All films will be screened in LARTS-111 at 7:00.
For questions write msneider@umassd.edu.
- Topical Areas: Faculty, Students, University Community, University Marketing
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12:00 AM
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1:00 AM
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Physics Master of Science Research Project by Zak Longinidis
- Location: > See description for location
- Contact: Physics Department
- Description: Topic: A Model For the N-Baryon Spectrum
Location: SENG -201
Abstract:
For our research this semester, we have been using a model developed by Dr. J.P. Hsu in order to approximate energy eigenvalues for given sub-spectra of N-baryon states. In particular, I have been taking rough estimates given by the model, and adjusting values for the coupling constant and potential energy in order to better fit the experimental data we are comparing to. I will also be giving a brief overview of the relativistic quantum shell model for confining 3-quark system, and talking about a possible range of light quark masses that are acceptable for quarks to have in order for this model to produce reasonable numerical results for baryon spectra.
Advisor(s): Dr. JP Hsu, Physics Department (jhsu@umassd.edu)
NOTE: All PHY Graduate Students are ENCOURAGED to attend. Open to the public. All interested parties are invited to attend.
- Topical Areas: Faculty, Staff and Administrators, Students, Students, Graduate, Students, Undergraduate
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4:30 PM
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6:30 PM
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Student Leadership Awards Celebration
- Location: > See description for location
- Contact: > See Description for contact information
- Description: Join us as we celebrate our outstanding student leaders and their accomplishments!
The Marketplace | UMass Dartmouth
4:30pm Hors D'oeuvres
5:00 pm Presentation of Awards
RSVP to claib@umassd.edu by Mon April 22, 2024
Contact: Chris Laib, claib@umassd.edu 508-999-8217
Sponsored by the Division of Student Affairs
- Topical Areas: Students, Black History 4 Seasons, Fredrick Douglass Unity House, Office of Diversity, Equity & Inclusion, University Marketing
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