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Title: Highly Robust Battery-Management System Design for Series Connected Lithium-ion Battery Packs
Creator: Zhang, Yunlong
Date: 2019
Description: Because of the manufacturing variances, the individual cells in a battery pack might have different capacities and be at different levels of...
Show moreBecause of the manufacturing variances, the individual cells in a battery pack might have different capacities and be at different levels of state-of-charge (SOC). Typically, battery balancing in the battery management system (BMS) is the process to equalize the level of SOC of each battery cell in the battery pack. Without effective and appropriate battery balancing, the smallest capacity cell will limit the energy that can be delivered from or charged into the battery pack. Besides, balancing process eliminates the potential of overcharge and overdischarge which is harmful for the battery life cycles and may result in the battery pack explosion. Lithium-ion rechargeable battery cells are rather more sensitive to over-charging/discharging and over-temperature than most commonly used battery chemistries. In this thesis, we proposed the efficiency optimization of the SOC based balancing for series connected lithium-ion battery packs.There are two categories of balancing methods, passive and active. In passive balancing, energy is dissipated through resistors as heat; in active balancing, energy is transferred from the most charged cell(s) to the least charged cell(s) with equalizer(s). Since the balancing efficiency of cell-to-cell (CTC) and cell-to-pack-to-cell (CPC) active methods is higher than any other balancing technique, our optimized balancing scheme in this thesis is implemented based on these two active methods. Because we need to design and manufacture the BMS Printed circuit board (PCB), we have to figure out one optimized balancing circuit by analyzing the different initial SOC distributions and multiple balancing topologies. Not only the minimal energy dissipation of balancing process, but also the structural hazard of different balancing topologies that we need to take into account in the balancing efficiency evaluation for different balancing topologies.OrCAD capture tool from Cadence is the widely used Electronic design automation (EDA) tool to simulate the function of designed real circuit. Because it is too complicated to provided the discrepant control signals for different equalizers in OrCAD, and the simulation runtime of OrCAD will increase exponentially with the increasing number of the balancing equalizers, it is necessary to design one novel computer-aided design (CAD) tool to decrease the simulation runtime for battery packs. Finally, we obtained the optimized balancing circuit by analyzing the balancing circuit efficiency with the novel Matlab based CAD tool. The prototype of BMS PCB we designed consists of microcontroller unit (MCU), Direct current (DC) to DC converter, active balancing circuit, CAN interface and power switches which is used for overcharge and over-discharge protection.
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Title: Application-Oriented Scheduling for Optimizing Information Freshness in Wireless Networks
Creator: Yin, Bo
Date: 2020
Description: Recent years have witnessed a significant advancement of networking technologies as well as the proliferation of mobile devices. Due to the...
Show moreRecent years have witnessed a significant advancement of networking technologies as well as the proliferation of mobile devices. Due to the convergence of pervasive connectivity and ubiquitous computing, Internet of Things (IoT) systems are becoming increasingly information-centric. For those IoT devices, wireless communication is the dominant way to exchange information. The development of IoT has spawned a plethora of real-time applications, boosting the demand for timely information updates. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. Due to its sheer novelty in capturing the timeliness requirements of various applications, AoI has sparked tremendous interest and been studied in many communication systems. This thesis aims at an exploratory study on how to characterize the essence of wireless scheduling for effective information freshness from the decision-making perspectives through two representative application scenarios, information retrieval and information integration. For the former, request-aware proactive scheduling policies in both static and dynamic request patterns are developed, which target at minimizing time-average effective AoI (EAoI). For the latter, an experience-driven scheduling framework based on deep reinforcement learning techniques is investigated to minimize the time-average AoI in the presence of correlated information sources. Future research directions are also discussed to present possible extensions of this thesis work to a broader range of network scenarios.
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Title: GAME THEORY BASED LOCATION-AWARE CHARGING SOLUTIONS FOR NETWORKED ELECTRIC VEHICLES
Creator: Laha, Aurobinda
Date: 2020
Description: The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of...
Show moreThe recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid (V2G) network, vehicles and charging stations can respectively make better charging and pricing decisions via real-time information sharing. In this research, we study the charging problem in an intelligent transportation system (ITS), which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location while the objective of a charging station is to maximize its revenue given the charging strategy of the vehicles. We employ a multileader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that there exists a unique equilibrium for the followers’ subgame played by the vehicles, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable through the proposed charging scheme. We further evaluate the price of anarchy (PoA) of the proposed charging scheme by using a centralized optimization model, in which a modified matching algorithm is applied. In state-of-the-art research works, PHEVs tend to charge or discharge to a smart grid individually. In our extended work, we also consider the discharging scenarios for PHEVs, which is generally during the peak hours of a micro-grid system. We propose that by leveraging the cooperation between charging and discharging PHEVs, the grid will be able to properly disperse the charging load in the load valley and discharging during the load peak hours. As a consequence, the electricity load will be well balanced. In this process, the PHEVs also receive greater benefit, thus serving the PHEV charging and discharging cooperation as a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of a coalition game. Finally, simulation results confirm the uniqueness of the equilibrium in both the game strategies. A performance comparison between the proposed distributed and centralized strategy with existing solutions are presented. We also provide the results of the coalition game when both charging and discharging PHEVs are present in the network. The proper management of charging and discharging of EVs poses one of the most challenging and interesting issues in our research. We aim to provide a complete demand response management solution to PHEVs and micro-grids in a real-time scenario.
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Title: AUTOMATION OF ULTRASONIC FLAW DETECTION APPLICATIONS USING DEEP LEARNING ALGORITHMS
Creator: Virupakshappa, Kushal
Date: 2021
Description: The Industrial Revolution-4.0 promises to integrate multiple technologies including but not limited to automation, cloud computing, robotics,...
Show moreThe Industrial Revolution-4.0 promises to integrate multiple technologies including but not limited to automation, cloud computing, robotics, and Artificial Intelligence. The non-Destructive Testing (NDT) industry has been shifting towards automation as well. For ultrasound-based NDT, these technological advancements facilitate smart systems hosting complex signal processing algorithms. Therefore, this thesis introduces the effective use of AI algorithms in challenging NDT scenarios. The first objective is to investigate and evaluate the performance of both supervised and unsupervised machine learning algorithms and optimize them for ultrasonic flaw detection utilizing Amplitude-scan (A-scan) data. Several inferences and optimization algorithms have been evaluated. It has been observed that proper choice of features for specific inference algorithms leads to accurate flaw detection. The second objective of this study is the hardware realization of the ultrasonic flaw detection algorithms on embedded systems. Support Vector Machine algorithm has been implemented on a Tegra K1 GPU platform and Supervised Machine Learning algorithms have been implemented on a Zynq FPGA for a comparative study. The third main objective is to introduce new deep learning architectures for more complex flaw detection applications including classification of flaw types and robust detection of multiple flaws in B-scan data. The proposed Deep Learning pipeline combines a novel grid-based localization architecture with meta-learning. This provides a generalized flaw detection solution wherein additional flaw types can be used for inference without retraining or changing the deep learning architecture. Results show that the proposed algorithm performs well in more complex scenarios with high clutter noise and the results are comparable with traditional CNN and achieve the goal of generality and robustness.
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Title: DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Creator: Khairy, Sami
Date: 2021
Description: The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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Title: Implementation of a multisensor wearable artificial pancreas platform: ensuring safety with communication robustness and cyber security
Creator: Lazaro Martinez, Carmen Caterina
Date: 2019
Description: Advances in IoT technologies and new sensor capabilities contributed to the rapid growth of wearable medical devices. Today, mobile sensor...
Show moreAdvances in IoT technologies and new sensor capabilities contributed to the rapid growth of wearable medical devices. Today, mobile sensor platforms can be effectively, cost efficiently integrated in healthcare applications. However, the increased risks of these devices, inherent vulnerabilities of mobile operating systems and open nature of the wireless protocols call for improved safety and security measures to prioritize patient's well-being. In the field of type 1 diabetes, blood glucose level management with insulin control algorithms are available in diabetes therapy systems, though none are fully automated and require extra announcements (such as meal and exercise) to operate. A mobile artificial pancreas (AP), based on Android smartphone, is developed: such a platform relies on off-the-shelf components and receives in real-time the physiological measurements from the wrist worn physical activity tracker and the glucose measurements, then used in a predictive control algorithm (originally developed and tested on a laptop), to compute the optimal amount of insulin to administer via an insulin pump. A dedicated remote server provides additional support for registration, authentication and data backup.The nature of the algorithm required a fast, reliable method to translate its inherent functions. Therefore, we implement a new semi-automatic conversion mechanism which ports MATLAB to Android as native C code. Validation tests of the mobile version confirm there are no deviations in the results.Moreover, in order to enhance safety guarantees for the patient, this cyber-physical system needs a robust implementation also resilient to attacks and failures. A central monitor module is introduced, wherein wireless devices and communications channels are integrated with complementary alarm and safety subsystems. The parameterization of the AP as a state machine demonstrates the efficiency to detect and react to possible errors, since any state change triggers the appropriate correcting response. The result is a protected and fail-safe environment, further expanded with security modules enforcing encryption, authenticated access and data-flow rules for intrusion detection.Overall, this research demonstrates, in the case of an AP, how challenges in diverse fields such as sensor fusion, control systems, wireless communications and cybersecurity can be addressed with a holistic approach for mobile health (mHealth).
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Title: Security analysis in device-to-device wireless networks
Creator: Liu, Kecheng
Date: 2019
Description: Device-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each...
Show moreDevice-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each other even when internet access is not available. Because D2D network is expected to be an intrinsic part of the Internet of Things (IoT) and mobile device is the smartest and the most advanced commercial device in everyday usage, the D2D feature and related security protocols can influence the design and implementation of many other IoT devices. While D2D network provides tangible benefits to users, it also raises the security risks of information leaking. Our work performs an in-depth systematical security analysis on 802.11 based D2D network among commercial devices, including personal mobile devices such as phones and tablet, as well as business POS and printers. In this paper, we focus on most popular apps in the Google Play Store, the best selling printers in the market and the most widely adopted commercial POS devices for small businesses. Our analysis reveals some critical vulnerabilities. The key findings are multi-fold. First, the current mobile D2D network framework established on 802.11 protocol has significant flaw of over-privileged issue. Second, we have identified that data transfer over D2D network can be eavesdropped. Furthermore, we exploit the identified framework flaws to construct multiple proof-of-concept attacks and we conclude the paper with security lessons and suggestions of possible solutions against the identified security issues.
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Title: A SCALABLE SIMULATION AND MODELING FRAMEWORK FOR EVALUATION OF SOFTWARE-DEFINED NETWORKING DESIGN AND SECURITY APPLICATIONS
Creator: Yan, Jiaqi
Date: 2019
Description: The world today is densely connected by many large-scale computer networks, supporting military applications, social communications, power...
Show moreThe world today is densely connected by many large-scale computer networks, supporting military applications, social communications, power grid facilities, cloud services, and other critical infrastructures. However, a gap has grown between the complexity of the system and the increasing need for security and resilience. We believe this gap is now reaching a tipping point, resulting in a dramatic change in the way that networks and applications are architected, developed, monitored, and protected. This trend calls for a scalable and high-fidelity network testing and evaluation platform to facilitate the transformation from in-house research ideas to real-world working solutions. With this objective, we investigate means to build a scalable and high-fidelity network testbed using container-based emulation and parallel simulation; our study focuses on the emerging software-defined networking (SDN) technology. Existing evaluation platforms facilitate the adoption of the SDN architecture and applications to production systems. However, the performance of those platforms is highly dependent on the underlying physical hardware resources. Insufficient resources would lead to undesired results, such as low experimental fidelity or slow execution speed, especially with large-scale network settings. To improve the testbed fidelity, we first develop a lightweight virtual time system for Linux container and integrate the system into a widely-used SDN emulator. A key issue with an ordinary container-based emulator is that it uses the system clock across all the containers even if a container is not being scheduled to run, which leads to the issue of both performance and temporal fidelity, especially with high workloads. We investigate virtual time approaches by precisely scaling the time of interactions between containers and physical devices. Our evaluation results indicate a definite improvement in fidelity and scalability. To improve the testbed scalability, we investigate how the centralized paradigm of SDN can be utilized to reduce the simulation workload. We explore a model abstraction technique that effectively transforms the SDN network devices to one virtualized switch model. While significantly reducing the model execution time and enabling the real-time simulation capability, our abstracted model also preserves the end-to-end forwarding behavior of the original network.With enhanced fidelity and scalability, it is realistic to utilize our network testbed to perform a security evaluation of various SDN applications. We notice that the communication network generates and processes a huge amount of data. The logically-centralized SDN control plane, on the one hand, has to process both critical control traffic and potentially big data traffic, and on the other hand, enables many efficient security solutions, such as intrusion detection, mitigation, and prevention. Recently, deep neural networks achieve state-of-the-art results across a range of hard problem spaces. We study how to utilize the big data and deep learning to secure communication networks and host entities. For classifying malicious network traffic, we have performed the feasibility study of off-line deep-learning based intrusion detection by constructing the detection engine with multiple advanced deep learning models. For malware classification on individual hosts, another necessity to secure computer systems, existing machine learning-based malware classification methods rely on handcrafted features extracted from raw binary files or disassembled code. The diversity of such features created has made it hard to build generic malware classification systems that work effectively across different operational environments. To strike a balance between generality and performance, we explore new graph convolutional neural network techniques to effectively yet efficiently classify malware programs represented as their control flow graphs.
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Title: Deep Learning Methods For Wireless Networks Optimization
Creator: Zhang, Shuai
Date: 2022
Description: The resurgence of deep learning techniques has brought forth fundamental changes to how hard problems could be solved. It used to be held that...
Show moreThe resurgence of deep learning techniques has brought forth fundamental changes to how hard problems could be solved. It used to be held that the solutions to complex wireless network problems require accurate mathematical modeling of the network operation, but now the success of deep learning has shown that a data-driven method could generate powerful and useful representations such that the problem could be solved efficiently with surprisingly competent performance. Network researchers have recognized this and started to capitalize on the learning methods’ prowess. But most works follow the existing black-box learning paradigms without much accommodation to the nature and essence of the underlying network problems. This thesis focuses on a particular type of classical problem: multiple commodity flow scheduling in an interference-limited environment. Though it does not permit efficient exact algorithms due to its NP-hard complexity, we use it as an entry point to demonstrate from three angles how the learning-based methods can help improve the network performance. In the first part, we leverage the graphical neural network (GNN) techniques and propose a two-stage topology-aware machine learning framework, which trains a graph embedding unit and a link usage prediction module jointly to discover links that are likely to be used in optimal scheduling. The second part of the thesis is an attempt to find a learning method that has a closer algorithmic affinity to the traditional DCG method. We make use of reinforcement learning to incrementally generate a better partial solution such that a high quality solution may be found in a more efficient manner. As the third part of the research, we revisit the MCF problem from a novel viewpoint: instead of leaning on the neural networks to directly generate the good solutions, we use them to associate the current problem instance with historical ones that are similar in structure. These matched instances’ solutions offer a highly useful starting point to allow efficient discovery of the new instance’s solution.
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Title: Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
Creator: Arnold, David James
Date: 2024
Description: Digitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To...
Show moreDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy. Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis. Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry.
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Title: Optimization methods and machine learning model for improved projection of energy market dynamics
Creator: Saafi, Mohamed Ali
Date: 2023
Description: Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon...
Show moreSince signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon emissions from the transportation sector, countries around the world have created a well-defined new energy vehicle development strategy that is further expanding into hydrogen vehicle technologies. In this study, we develop the Transportation Energy Analysis Model (TEAM) to investigate the impact of the CO2 emissions policies on the future of the automotive industries. On the demand side, TEAM models the consumer choice considering the impacts of technology cost, energy cost, refueling/charging availability, consumer travel pattern. On the supply side, the module simulates the technology supply by the auto-industry with the objective of maximizing industry profit under the constraints of government policies. Therefore, we apply different optimization methods to guarantee reaching the optimal automotive industry response each year up to 2050. From developing an upgraded differential evolution algorithm, to applying response surface methodology to simply the objective function, the goal is to enhance the optimization performance and efficiency compared to adopting the standard genetic algorithm. Moreover, we investigate TEAM’s robustness by applying a sensitivity analysis to find the key parameters of the model. Finally based on the key sensitive parameters that drive the automotive industry, we develop a neural network to learn the market penetration model and predict the market shares in a competitive time by bypassing the total cost of ownership analysis and profit optimization. The central motivating hypothesis of this thesis is that modern optimization and modeling methods can be applied to obtain a computationally-efficient, industry-relevant model to predict optimal market sales shares for light-duty vehicle technologies. In fact, developing a robust market penetration model that is optimized using sophisticated methods is a crucial tool to automotive companies, as it quantifies consumer’s behavior and delivers the optimal way to maximize their profits by highlighting the vehicles technologies that they could invest in. In this work, we prove that TEAM reaches the global solution to optimize not only the industry profits but also the alternative fuels optimized blends such as synthetic fuels. The time complexity of the model has been substantially improved to decrease from hours using the genetic algorithm, to minutes using differential evolution, to milliseconds using neural network.
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