College of Engineering at UMass Dartmouth

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Topic: Multi-Modality Sensing and Communications for Connected Vehicles Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/92374869463 Meeting ID: 923 7486 9463 Passcode: 174802 Abstract: Connected and autonomous vehicles (CAVs) are set to transform intelligent transportation systems by enhancing safety, traffic efficiency, and mobility. Key use cases require high throughput, low latency, reliable communication, and precise positioning. The millimeter-wave (mmWave) band offers a broad spectrum to meet these demands but suffers from high path attenuation, necessitating beamforming techniques that use large antenna arrays to create narrow beams for better signal strength. Traditional beam alignment methods based on exhaustive searching can result in significant computational and communication overhead, making them unsuitable for dynamic vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scenarios. This proposal outlines background information, problem statements, and the challenges of deploying mmWave communications in CAV use cases. It includes two preliminary solutions that utilize deep learning techniques, leveraging single and multi-modality sensing data to predict optimal beams for strong mmWave links. Real-world testing indicates that these methods significantly reduce the beam searching time and overhead compared to conventional approaches, representing a promising advancement in mmWave communication. The proposal concludes with suggested future directions for research. Co-Advisor(s): Dr. Honggang Wang, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Hua Fang, Professor, Dept. of Computer & Information Science, UMASS Dartmouth Committee Members: Dr. Mohammad Karim, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Liudong Xing, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Qing Yang, Associate Professor, Dept. of Computer Science and Engineering, University of North Texas 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. Honggang Wang via email at hwang1@umassd.edu.

Science & Engineering Building, Lester W. Cory Conference Room: Room 213A

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Grand Reading Room

Topic: A Heterogeneous Multi-Agent Colluding Attack Defense System Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/99742168771 Meeting ID: 997 4216 8771 Passcode: 976327 Abstract: The voting-based replication mechanism is widely used as a fault-tolerant technique across various engineering and computing domains. However, this technique is vulnerable to colluding attacks where multiple malicious resources can collaborate to produce identical wrong results to potentially fail a task execution, posing significant threats to system integrity, performance, and reliability. This research introduces a heterogeneous multi-agent colluding attack defense system, a novel credibility-based framework designed to detect and mitigate the impact of coordinated adversarial behavior within diverse agent environments. The proposed framework employs a dual-role architecture, consisting primarily of spotter and resource agents. Spotters are responsible for monitoring and evaluating the credibility of resource agents based on their performance and voting patterns. Resource agents that fail the spotter's check are excluded from task execution. The system dynamically and optimally allocates these roles under users' predefined cost constraints, balancing resource utilization and defense efficiency. By strategically adjusting the credibility scores of resource agents, the proposed defense mechanism adapts to ensure sustained system performance and reliability. Experimental studies are conducted to demonstrate the effectiveness of the proposed heterogeneous multi-agent system in defending against colluding attacks in voting-based computing environments. The impacts of several key model parameters on the system reliability are also investigated. Co-Advisor(s): Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Jiawei Yuan, Associate Professor, Department of Computer & Information Science, 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. Liudong Xing via email at lxing@umassd.edu or Dr. Lance Fiondella via email at lfiondella@umassd.edu

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Spring 2025 Add period and Drop period end for the Accelerated Nursing Session 1.

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