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DTSTART;TZID=America/New_York:20220721T090000
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DTSTAMP:20260413T182058
CREATED:20221103T142650Z
LAST-MODIFIED:20221103T142650Z
UID:34106-1658394000-1658397600@coe.northeastern.edu
SUMMARY:Abhimanyu Sheshashayee's PhD Dissertation Defense
DESCRIPTION:Location: 532 ISEC \n“Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios” \nAbstract: \nMulti-hop wireless networks such as Wireless Sensor Networks and in general\, networks without the support of a fixed infrastructure\, which enable most applications of the Internet of Things\, are comprised of wirelessly communicating nodes that are often powered by batteries. In many relevant scenarios—ranging from precision agriculture to oceanographic surveillance—it is inconvenient or impossible to replenish or replace the energy systems of these nodes\, which limits the operational lifespan of the network. One of the most significant sources of power consumption comes from idle listening on the node’s wireless transceiver (main radio). This consumption can be reduced by endowing the nodes with Wake-up Radio (WuR) technology: Nodes keep their main radio off while listening for a signal via an ultra-low-power auxiliary radio used only for wake-up purposes. When the appropriate signal is received\, the node turns its main radio on\, conducts the necessary exchange of packets\, and then turns off its main radio. This strategy allows for a considerable reduction in power consumption.This dissertation investigates data collection approaches that leverage WuR technology to maximize the lifespan of multi-hop networks for data gathering\, via routing and via a Mobile Data Collector (MDC). We analyze contemporary WuR technology\, isolating the main criticalities of the state-of-the-art\, including range and data rates. We use WuR prototypes with highly desirable characteristics to conduct experiments to measure effective communication ranges\, in both static and mobile scenarios. We then examine the application of WuR technology to data collection based on multi-hop routing. We devise new techniques and evaluate the effects of different WuR characteristics on the performance of routing\, considering for the first time what the network performance could be if we could overcome the limitation of current WuRs.The culmination of this dissertation focuses on mobile data collection protocols and approaches. We conduct a comprehensive survey of mobile data collection studies and protocols. We develop a robust taxonomy to set the framework for our analyses of various methodologies and elements of mobile data collection. Guided by our review of the literature\, we define two collection strategies: a simple naïve strategy\, and a novel AI-driven adaptive strategy. Both strategies leverage WuR technology to minimize the amount of time SNs remain awake. Considering both duty cycle-based and WuR based scenarios\, we conduct extensive experiments with a quad-rotor UAV-MDC and a network of WuR-enabled wireless sensor motes. We replicate these experiments in our simulator\, informed by the parameters and characteristics observed in our real-world experiments. Having validated our simulations\, we proceed to execute exhaustive simulation-based experiments. We evaluate the effects of scale (namely\, network size and deployment region size) on the performance of the naïve and adaptive strategies\, and we contrast the energy efficiency. The WuR-based scenarios experience considerably lower time spent awake\, which gives rise to longer network lifespan. The adaptive strategy minimizes the time taken for each collection cycle\, thereby reducing the amount of time spent awake in the duty cycle-based scenarios. The adaptive strategy also results in a noticeable reduction in both the awake duration and latency for the WuR-based scenarios. \nCommittee: \nProfessor Stefano Basagni (Advisor)Professor Kaushik ChowdhuryProfessor Tommaso Melodia
URL:https://coe.northeastern.edu/event/abhimanyu-sheshashayees-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20220721T143000
DTEND;TZID=America/New_York:20220721T153000
DTSTAMP:20260413T182059
CREATED:20221103T142855Z
LAST-MODIFIED:20221103T142855Z
UID:34101-1658413800-1658417400@coe.northeastern.edu
SUMMARY:Siyue Wang's PhD Dissertation Defense
DESCRIPTION:“Towards Robust and Secure Deep Learning Models and Beyond” \nAbstract: \nModern science and technology witness the breakthroughs of deep learning during the past decades. Fueled by the rapid improvements of computational resources\, learning algorithms\, and massive amounts of data\, deep neural networks (DNNs) have played a dominant role in many real world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies have revealed the limitations of DNNs which raise safety and reliability concerns of its widespread usage: 1) the robustness of DNN models under adversarial attacks and facing instability problems of edge devices\, and 2) the protection and verification of intellectual properties of well-trained DNN models.In this dissertation\, we first investigate how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean inputs can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). Besides\, we also propose a stochastic fault-tolerant training scheme that can generally improve the robustness of DNNs when facing the instability problem on DNN accelerators without focusing on optimizations for individual devices.The second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. To better perform functionality verification of DNNs implemented on edge devices for on-device inference applications\, we also propose Intrinsic Examples. Intrinsic Examples as fingerprinting of DNN can detect adversarial third-party attacks that embed misbehaviors through re-training. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set. \nCommittee: \nProf. Xue Lin (Advisor)Prof. Yunsi FeiProf. Yanzhi Wang
URL:https://coe.northeastern.edu/event/siyue-wangs-phd-dissertation-defense/
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