College of Engineering at UMass Dartmouth

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

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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

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Observatory Open House For updates on weather conditions please refer to www.assne.org

UMass Dartmouth Observatory Field

Spring 2024 14-week full session classes end, last class before final exam.

Online

Spring classes end today.

UMass Dartmouth

Mechanical Engineering (MNE) Senior Design (Capstone) Presentations April 30, 2024 9:00 a.m. - 4:30 p.m. (Poster and prototype preview begins at 8:00 a.m.) Woodland Commons The Mechanical Engineering Department is proud to share this highly anticipated event with students, faculty, staff, family, friends, and any other interested guests! This is a culmination of the Class of 2024's Senior year team project with industry, or UMD research faculty. Attend all day, or come and go as your schedule allows. For more information please contact Dr. Hamed Samandari/Instructor (hsamandari@umassd.edu) or Sue Cunha/Administrative Assistant (scunha@umassd.edu).

Woodland Commons, UMass Dartmouth Campus