Browsing by Author "Simske, Steve, committee member"

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Open Access
An analysis of Internet of Things (IOT) ecosystem from the perspective of device functionality, application security and application accessibility
(Colorado State University. Libraries, 2022) Paudel, Upakar, author; Ray, Indrakshi, advisor; Malaiya, Yashwant, committee member; Simske, Steve, committee member
Internet of Thing (IoT) devices are being widely used in smart homes and organizations. IoT devices can have security vulnerabilities in different fronts: Device front with embedded functionalities and Application front. This work aims to analyze IoT devices security health from device functionality perspective and application security and accessibility perspective to understand holistic picture of entire IoT ecosystem's security health. An IoT device has some intended purposes, but may also have hidden functionalities. Typically, the device is installed in a home or an organization and the network traffic associated with the device is captured and analyzed to infer high-level functionality to the extent possible. However, such analysis is dynamic in nature, and requires the installation of the device and access to network data which is often hard to get for privacy and confidentiality reasons. In this work, we propose an alternative static approach which can infer the functionality of a device from vendor materials using Natural Language Processing (NLP) techniques. Information about IoT device functionality can be used in various applications, one of which is ensuring security in a smart home. We can also use the device functionalities in various security applications especially access control policies. Based on the functionality of a device we can provide assurance to the consumer that these devices will be compliant to the home or organizational policy even before they have been purchased. Most IoT devices interface with the user through mobile companion apps. Such apps are used to configure, update, and control the device(s) constituting a critical component in the IoT ecosystem, but they have historically been under-studied. In this thesis, we also perform security and accessibility analysis of IoT application on 265 apps to understand security and accessibility vulnerabilities present in the apps and identify some mitigating strategies.
Open Access
Applying model-based systems engineering in search of quality by design
(Colorado State University. Libraries, 2022) Miller, Andrew R., author; Herber, Daniel R., advisor; Bradley, Thomas, committee member; Miller, Erika, committee member; Simske, Steve, committee member; Yalin, Azer P., committee member
Model-Based System Engineering (MBSE) and Model-Based Engineering (MBE) techniques have been successfully introduced into the design process of many different types of systems. The application of these techniques can be reflected in the modeling of requirements, functions, behavior, and many other aspects. The modeled design provides a digital representation of a system and the supporting development data architecture and functional requirements associated with that architecture through modeling system aspects. Various levels of the system and the corresponding data architecture fidelity can be represented within MBSE environment tools. Typically, the level of fidelity is driven by crucial systems engineering constraints such as cost, schedule, performance, and quality. Systems engineering uses many methods to develop system and data architecture to provide a representative system that meets costs within schedule with sufficient quality while maintaining the customer performance needs. The most complex and elusive constraints on systems engineering are defining system requirements focusing on quality, given a certain set of system level requirements, which is the likelihood that those requirements will be correctly and accurately found in the final system design. The focus of this research will investigate specifically the Department of Defense Architecture Framework (DoDAF) in use today to establish and then assess the relationship between the system, data architecture, and requirements in terms of Quality By Design (QbD). QbD was first coined in 1992, Quality by Design: The New Steps for Planning Quality into Goods and Services [1]. This research investigates and proposes a means to: contextualize high-level quality terms within the MBSE functional area, provide an outline for a conceptual but functional quality framework as it pertains to the MBSE DoDAF, provides tailored quality metrics with improved definitions, and then tests this improved quality framework by assessing two corresponding case studies analysis evaluations within the MBSE functional area to interrogate model architectures and assess quality of system design. Developed in the early 2000s, the Department of Defense Architecture Framework (DoDAF) is still in use today, and its system description methodologies continue to impact subsequent system description approaches [2]. Two case studies were analyzed to show proposed QbD evaluation to analyze DoDAF CONOP architecture quality. The first case study addresses the analysis of DoDAF CONOP of the National Aeronautics and Space Administration (NASA) Joint Polar Satellite System (JPSS) ground system for National Oceanic and Atmospheric Administration (NOAA) satellite system with particular focus on the Stored Mission Data (SMD) mission thread. The second case study addresses the analysis of DoDAF CONOP of the Search and Rescue (SAR) navel rescue operation network System of Systems (SoS) with particular focus on the Command and Control signaling mission thread. The case studies help to demonstrate a new DoDAF Quality Conceptual Framework (DQCF) as a means to investigate quality of DoDAF architecture in depth to include the application of DoDAF standard, the UML/SysML standards, requirement architecture instantiation, as well as modularity to understand architecture reusability and complexity. By providing a renewed focus on a quality-based systems engineering process when applying the DoDAF, improved trust in the system and data architecture of the completed models can be achieved. The results of the case study analyses reveal how a quality-focused systems engineering process can be used during development to provide a product design that better meets the customer's intent and ultimately provides the potential for the best quality product.
Embargo
Automated extraction of access control policy from natural language documents
(Colorado State University. Libraries, 2023) Alqurashi, Saja, author; Ray, Indrakshi, advisor; Ray, Indrajit, committee member; Malaiya, Yashwant, committee member; Simske, Steve, committee member
Data security and privacy are fundamental requirements in information systems. The first step to providing data security and privacy for organizations is defining access control policies (ACPs). Security requirements are often expressed in natural languages, and ACPs are embedded in the security requirements. However, ACPs in natural language are unstructured and ambiguous, so manually extracting ACPs from security requirements and translating them into enforceable policies is tedious, complex, expensive, labor-intensive, and error-prone. Thus, the automated ACPs specification process is crucial. In this thesis, we consider the Next Generation Access Control (NGAC) model as our reference formal access control model to study the automation process. This thesis addresses the research question: How do we automatically translate access control policies (ACPs) from natural language expression to the NGAC formal specification? Answering this research question entails building an automated extraction framework. The pro- posed framework aims to translate natural language ACPs into NGAC specifications automatically. The primary contributions of this research are developing models to construct ACPs in NGAC specification from natural language automatically and generating a realistic synthetic dataset of access control policies sentences to evaluate the proposed framework. Our experimental results are promising as we achieved, on average, an F1-score of 93 % when identifying ACPs sentences, an F1-score of 96 % when extracting NGAC relations between attributes, and an F1-score of 96% when extracting user attribute and 89% for object attribute from natural language access control policies.
Open Access
Big Data decision support system
(Colorado State University. Libraries, 2022) Ma, Tian J., author; Chong, Edwin, advisor; Simske, Steve, committee member; Herber, Daniel, committee member; Pezeshki, Ali, committee member
Each day, the amount of data produced by sensors, social and digital media, and Internet of Things is rapidly increasing. The volume of digital data is expected to be doubled within the next three years. At some point, it might not be financially feasible to store all the data that is received. Hence, if data is not analyzed as it is received, the information collected could be lost forever. Actionable Intelligence is the next level of Big Data analysis where data is being used for decision making. This thesis document describes my scientific contribution to Big Data Actionable Intelligence generations. Chapter 1 consists of my colleagues and I's contribution in Big Data Actionable Intelligence Architecture. The proven architecture has demonstrated to support real-time actionable intelligence generation using disparate data sources (e.g., social media, satellite, newsfeeds). This work has been published in the Journal of Big Data. Chapter 2 shows my original method to perform real-time detection of moving targets using Remote Sensing Big Data. This work has also been published in the Journal of Big Data and it has received an issuance of a U.S. patent. As the Field-of-View (FOV) in remote sensing continues to expand, the number of targets observed by each sensor continues to increase. The ability to track large quantities of targets in real-time poses a significant challenge. Chapter 3 describes my colleague and I's contribution to the multi-target tracking domain. We have demonstrated that we can overcome real-time tracking challenges when there are large number of targets. Our work was published in the Journal of Sensors.
Open Access
Bistable prestressed spring steel grippers for aerial perching and grasping
(Colorado State University. Libraries, 2024) Jones, Bryce, author; Zhao, Jianguo, advisor; Ciarcia, Marco, committee member; Simske, Steve, committee member
Quadcopter drones are popular in both the consumer and commercial markets, with a wide range of uses and applications, including inspections, research, natural disaster response, and filming and photography. These uses and applications are currently limited, however, by the limited battery power and range of current drones. Aerial perching can extend the useful flight time of a drone by allowing for passive perching in a location for a desired amount of time. Compliant bistable mechanisms are well-suited for this application because of their adaptability in a wide range of environments while utilizing bistability to reduce energy consumption and complexity. Current research into aerial perching with compliant mechanisms is limited to heavy, rigid grippers with limited applications in a wide variety of environments and grippers with complicated pneumatic controls. In this thesis, we propose a novel solution to this gap in research through the use of prestressed spring steel bands (PSSB) to create compliant bistable grippers for aerial perching and grasping. We investigate multiple different PSSB configurations. We first investigate two single PSSB gripper designs, a single band gripper with a cable driven opening system, then an improved silicone encased single PSSB gripper design. The first single band gripper is experimentally tested to determine the triggering force, effect of offset on triggering force, effect of spring pretension on triggering force, opening force, grasping force, and activation time. This design had some issues with opening reliably and tangling. The improved silicone encased gripper is experimentally tested for triggering force, the effect of varying contact points and angles, activation time, reduction of triggering force with springs, and actual flight tests on a drone done in partnership with IIIT Hyderabad. The single band gripper designs can grasp a variety of objects, especially cylindrical ones, but are limited in grasping spherical and heavier objects, and vertical grasping. We then design a cross-shaped gripper based on the silicone encased PSSB gripper. This gripper is experimentally tested in the same manner as the silicone encased single band gripper and performs well in grasping spherical objects and vertical grasping. It does, however, struggle to grasp longer cylindrical objects. These gripper designs have a fixed triggering force based on the design that limited the applications for drone applications with high acceleration causing inadvertent activation, as well as for grasping lightweight objects. Being able to actively control the triggering force of the grippers would give the ability to tune the gripper for ideal performance in a wide range of applications. To actively tune the triggering force, we investigate the use of on the fly tuning with Nitinol Shape Memory Alloy springs. We first attempted closed-loop control by using a PID controller to control the resistance of the springs. Then we used an open loop control method where constant voltage is applied to the springs that allows for precise tuning of the triggering force to a set range for the desired application, and experimentally verify the reduction in triggering force and show the application of on the fly triggering force tuning.
Open Access
Cybersecurity vulnerabilities in electronic logging devices and development of a software defined truck testbed
(Colorado State University. Libraries, 2024) Jepson, Jacob, author; Daily, Jeremy, advisor; Simske, Steve, committee member; Ray, Indrajit, committee member
This thesis addresses critical cybersecurity vulnerabilities in Electronic Logging Devices (ELDs), mandated equipment for modern commercial trucks, and introduces an innovative solution for comprehensive system testing. Through extensive reverse engineering and practical testing, significant security flaws in commonly used ELDs are uncovered. These vulnerabilities enable unauthorized control over vehicle systems through arbitrary CAN message injection, allow upload of malicious firmware, and most alarmingly, present the potential for a self-propagating truck-to-truck worm. To demonstrate these vulnerabilities, bench-level testing and real-world experiments were conducted using a 2014 Kenworth T270 Class 6 research truck equipped with a vulnerable ELD. The findings reveal how these security weaknesses could lead to widespread disruptions in commercial fleets, with severe safety and operational implications. Addressing the fundamental challenge of disparate design and testing of after-market systems in trucks, this research introduces CANLay, a key networking component of the Software Defined Truck (SDT) concept. CANLay enables the virtualization of in-vehicle networks, facilitating the transportation of Controller Area Network (CAN) data and sensor signals over long-distance networks. This innovation allows for holistic security assessments and efficient testing of integrated vehicle systems, accounting for emergent behaviors that arise from system integration. The efficacy of CANLay in heavy vehicle network performance testing is demonstrated, showcasing its potential to streamline system integration and verification efforts in a versatile digital engineering environment. This work contributes to the field by illuminating current vulnerabilities in mandated trucking technology, demonstrating potential attack vectors, and providing a framework for more comprehensive and efficient testing of integrated vehicle systems. This research underscores the urgent need to improve the security posture of ELD systems and offers recommendations for enhancing their security. The findings and proposed solutions have significant implications for improving cybersecurity in the trucking industry and, by extension, safeguarding critical supply chains.
Open Access
Detecting non-secure memory deallocation with CBMC
(Colorado State University. Libraries, 2021) Singh, Mohit K., author; Prabhu, Vinayak, advisor; Ray, Indrajit, advisor; Ghosh, Sudipto, committee member; Ray, Indrakshi, committee member; Simske, Steve, committee member
Scrubbing sensitive data before releasing memory is a widely recommended but often ignored programming practice for developing secure software. Consequently, sensitive data such as cryptographic keys, passwords, and personal data, can remain in memory indefinitely, thereby increasing the risk of exposure to hackers who can retrieve the data using memory dumps or exploit vulnerabilities such as Heartbleed and Etherleak. We propose an approach for detecting a specific memory safety bug called Improper Clearing of Heap Memory Before Release, referred to as Common Weakness Enumeration 244. The CWE-244 bug in a program allows the leakage of confidential information when a variable is not wiped before heap memory is freed. Our approach uses the CBMC model checker to detect this weakness and is based on instrumenting the program using (1) global variable declarations that track and monitor the state of the program variables relevant for CWE-244, and (2) assertions that help CBMC to detect unscrubbed memory. We develop a tool, SecMD-Checker, implementing our instrumentation based algorithm, and we provide experimental validation on the Juliet Test Suite that the tool is able to detect all the CWE-244 instances present in the test suite. The proposed approach has the potential to work with other model checkers and can be extended for detecting other weaknesses that require variable tracking and monitoring, such as CWE-226, CWE-319, and CWE-1239.
Embargo
Determining systems engineering value in competitive bids
(Colorado State University. Libraries, 2023) Dawson, Sandra Lynn, author; Batchelor, Ann, advisor; Arenson, David, committee member; Adams, James, committee member; Simske, Steve, committee member; Wise, Dan, committee member
Corporations need a methodology to determine existing and new Systems Engineering (SE) effort costs in a more relevant context through deepening its connection within the competitive bidding process. The impact of Digital Engineering (DE) on SE within competitive bids is evolving as the industry is maturing its DE transition and implementation. The state of the field does not consider the impact of the current transition from traditional document-based SE (TDSE) to digital engineering (DE) and the impact on SE value. This paper examines the effectiveness of the SE costing models that are available in the literature by introducing a process to compare completed projects using metrics of actual SE hours expended and project performance against recommended SE effort and project results. Analysis of this comparison provides justification for SE effort bid ranges and associated project results. This research endeavors to enable a more holistic and SE-centric view of SE costing with considerations of project characteristics and the ongoing DE transition. Finally, this research provides a new framework for the analysis results and references useful in the bidding of SE projects where SE bid options can be associated with project performance, DE transition progress, and references relevant to the competitive bid approach. By applying systems thinking, using feedback loops and data from multiple organizations, understanding SE-DE impact, and empowering engineers in the DE transition, these research results enable data-driven decisions to determine SE value in competitive bids and to optimize SE using risk management. Following this process and using an organization's data (for competitive bids and projects) will yield results specific to competitive bids, bid technical approach, and DE transition progress. These results are communicated to the competitive bids teams using a SE focused framework.
Open Access
Evaluating micromobility adoption, perception, and implementation
(Colorado State University. Libraries, 2024) Pourfalatoun, Shiva, author; Gallegos, Erika, advisor; Daily, Jeremy, committee member; Simske, Steve, committee member; Bradley, Thomas, committee member; Jin, Ziyu, committee member
Micromobility, a term that encompasses compact and efficient transportation modes such as bicycles and scooters, has rapidly emerged as an important element of urban mobility. These small, often electrically-powered vehicles offer a versatile solution to urban congestion and provide an eco-friendly alternative to traditional transportation modes. Particularly, shared bicycles and e-scooters have become popular due to their convenience and accessibility, offering significant benefits but also presenting new challenges in urban planning and traffic management. This transition in urban transport paradigms raises several pertinent questions about user behaviors, preferences, and the interplay of various socio-psychological factors. This dissertation aims to explore three key aspects of micromobility. The first research question investigates the differences between shared e-scooter users and non-users, along with the factors influencing their decisions regarding e-scooter usage. The second question examines the shift in micromobility preferences and perceptions before, during, and after the COVID-19 pandemic, focusing on how these changes correlate with different quarantine behaviors. The third and final question delves into the interactions between drivers, bicyclists, and pedestrians, analyzing how drivers' risk-taking propensity and emotional intelligence influence these interactions. Each of these questions is approached through specific methodological frameworks, employing a mix of statistical analyses and behavioral observations to provide insights into the evolving dynamics of urban mobility. The findings from this research provide a systematic approach to integrating micromobility, by understanding at the individual level the factors that effect decision-making on usage, as well as interaction effects with other road users that impact safety.
Open Access
Evaluating the sustainability performance of U.S. biofuel in 2017 with an integrated techno-economic and life cycle assessment framework
(Colorado State University. Libraries, 2022) Smith, Jack Philip, author; Quinn, Jason, advisor; Simske, Steve, committee member; Bandhauer, Todd, committee member
The United States produced more than 66.2 million m3 of biofuel for the transportation industry in 2017. Most of that volume (60.6 million m3) was produced in the form of corn ethanol and the majority of the remaining volume (4.2 million m3) was produced in the form of soybean-based biodiesel. Numerous works have assessed the economic and environmental performance of these two biofuel types. However, no work exists which evaluates both the economic and environmental outcomes of these two fuels with adequate geospatial resolution and national scope. In this study, a model framework is constructed that performs concurrent Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) using high-resolution input datasets to provide a granular estimation of sustainability performance of every county in the United States. This work presents results that include sector wide estimates and highlights the importance of capturing geographic heterogeneity. Results show a total emission volume of 55 MMT CO2-eq produced by the 2017 US biofuel industry, with 7 MMT CO2-eq of that amount resulting from Land Use Change effects. Nationwide weighted mean Global Warming Potential results are 38 gCO2-eq/MJ and 37 gCO2-eq/MJ for corn ethanol and soybean biodiesel, respectively, when Land Use Change emissions are included. Minimum Fuel Selling Price results are $0.0208/MJ ($2.52/GGE) and $0.0225/MJ ($2.72/GGE) for corn ethanol and soybean biodiesel, respectively. A Zero-Emissions Cost (ZEC) metric is applied, which combines the economic and environmental performance of a fuel into its analysis. Specifically, the cost associated with offsetting all fuel production and use emissions through Direct Air Capture (DAC) is added to the standard price of the fuel. Mean ZEC results are $0.037/MJ ($4.53/GGE) for corn ethanol and $0.039/MJ ($4.69/GGE) for soybean biodiesel which are lower than the ZEC of conventional gasoline of $0.062/MJ ($7.45/GGE). Finally, the cost of Direct Air Capture which results in ZEC parity between each biofuel and its petroleum-based counterpart is assessed to be $49/MT CO2-eq.
Open Access
Exploration based design methodology using the theory of constraints in extending plastics manufacturing for novel high performing fabrics
(Colorado State University. Libraries, 2022) Shekoni, Aderemi, author; Troxell, Wade, advisor; Simske, Steve, committee member; Young, Peter, committee member; Prieto, Amy, committee member
The world of textiles is comprised of several materials. From the conventional, such as cotton and silk, to the contemporary, such as polyester and nylon, textiles have changed over time. Nonwovens, a category of material frequently referred to as the "third-generation" of textiles, have emerged as one of the most exciting breakthroughs in the textile industry during the past few years. Nonwovens, which are frequently confused with fibers, yarns, and fabrics, have evolved as a new category of versatile material with medicinal and industrial applications. An issue associated with the use of lightweight nonwovens is their single-use, in which a fabric weight category can be employed for only one product. The number of products per weight class that can be utilized in businesses that utilize the materials is limited. Therefore, companies utilizing these textiles in their operations must engage with plastic producers to plan, implement, and develop a single weight class for a single product. This procedure is time-consuming and generates plastic waste because of unfinished fabrics. By creating a multipurpose nonwoven fabric, organizations will be able to improve their operations by saving time and energy, improving profits, decreasing plastic waste, and enabling process innovation. To use a fabric with the same weight and similar physical properties in a different product, a different fabric is manufactured for that process, despite the similarity in weight and physical properties between the fabric used in the previous process and the fabric needed for the new process. Due to this limitation, the concept of redesigning nonwoven materials for different applications was conceived. Air Permeability, a barrier to airflow, is a significant component in the inability to support numerous uses. When a fabric's desired attribute is not satisfied, the fabric's air permeability can be optimized by utilizing a variety of process approaches to attain the appropriate performance qualities. This permits the use of a single fabric in a variety of items. Due to the fabric's weight and volume, the usage of nonwoven in aviation and public works has expanded drastically. Thermal insulation is one of the most prevalent applications of nonwoven materials in the aviation industry. Nonwoven fabrics are also utilized as dynamic biofilters for filtration in public works, with an aerobic layer that aids in the recovery of alkalinity in the filtration systems used in these facilities. The two significant outcomes of this research are (1) Improvement of the airflow barrier, also known as air permeability (AP), which enables the use of a single weight class to make several goods as opposed to a single weight class for a single product, and the addition of a thermal barrier to the fabric. Permeability enhancements in nonwovens enhance the fabric's sound absorption, filtration, and heat absorption. (2) The capacity to recycle undesired nonwoven fabrics following production, as opposed to disposing of the plastic components in landfills. Nonwovens are semi-crystalline polypropylene plastics that are not easily biodegradable due to the strong chemical bond between the polypropylene polymers. Because polypropylenes, which are plastics, are not biodegradable, unused nonwoven fabrics are landfilled. It was through the process of prototyping that a subsystem alteration was made that enabled the development of nonwoven fabric with better air permeability. Design as Exploration concepts are used to accomplish this. Reicofil I, II, III, and IV are the four nonwoven production systems used in this research to develop the novel fabric. In addition, this study has handled another issue by reusing and recycling unwanted fabrics to reduce the amount of plastic waste in landfills. An extrusion method that recycles rejected and waste fabrics were the result of these approaches. The innovative method used in developing the new nonwoven fabric is being explored for use in the production of plastic films to improve the quality of goods made with polyethylene plastic polymers.
Open Access
Framework for optimizing survivability in complex systems
(Colorado State University. Libraries, 2024) Younes, Megan Elizabeth, author; Cale, James, advisor; Gallegos, Erika, committee member; Simske, Steve, committee member; Gaofeng, Jia, committee member
Increasing high probability low frequency events such as extreme weather incidents in combination with aging infrastructure in the United States puts the nation's critical infrastructure such as hydroelectric dams' survivability at risk. Maximizing resiliency in complex systems can be viewed as a multi-objective optimization that includes system performance, survivability, economic and social factors. Systems requiring high survivability: a hydroelectric dam, typically require one or more redundant (standby) subsystems, which increases system cost. To optimize the tradeoffs between system survivability and cost, this research introduces an approach for obtaining the Pareto-optimal set of design candidates ("resilience frontier"). The method combines Monte Carlo (MC) sampling to estimate total survivability and a genetic algorithm (GA), referred to as the MCGA, to obtain the resilience frontier. The MCGA is applied to a hydroelectric dam to maximize overall system survivability. The MCGA is demonstrated through several numerical case studies. The results of the case studies indicate that the MCGA approach shows promise as a tool for evaluating survivability versus cost tradeoffs and also as a potential design tool for choosing system configuration and components to maximize overall system resiliency.
Open Access
Fully integrated network of networks
(Colorado State University. Libraries, 2022) LaMar, Suzanna, author; Jayasumana, Anura, advisor; Cale, Jim, committee member; Guo, Yanlin, committee member; Simske, Steve, committee member
There are many different facets to developing a fully integrated network of networks system that can facilitate seamless information exchange between nodes within a complex network topology. As an example, individual link resiliency, enhanced waveform capabilities, spectral and spatial diversity are all critical features in providing communications that can enable connectivity and interoperability for a fully networked system extending into multiple domains (ground, surface, air, and space). Steps taken toward achieving such an architecture are introduced with emerging millimeter wave (mmW) and high-band antenna technologies that can be integrated with future tactical multifunction software defined radios (SDRs) to enable information distribution between vital networked participants, including 5th generation aircraft. Small, lightweight mmW and high-band antenna designs that will enable small unit tactical operations to persist under electronic warfare conditions will be discussed. These small units are typically fielded with multiple communications radios but are limited in function and do not enable rapid communication on the move, or high-capacity data transfers at the halt. Additionally, a revolutionary cognitive antenna (CA) is introduced where artificial intelligence (AI) techniques are proposed to aid in improving antenna functions, support self-healing attributes, and promote autonomous communication operations. A CA designed for future spacecraft (S/C) communications systems that is environmentally perceptive will be presented where it can sense and transmit radio frequency (RF) signals and cooperate with a cognitive radio (CR) to modify waveform and beam pattern characteristics for enhanced resiliency and communications. As an extrapolation to interoperability and information exchanges, data must be always secured. Common communications payload security architectures are presented as a basis for offering data protection to not only the system itself, but also to networks that are part of the larger enterprise solution. Similarly, machine learning methods are proposed to combat malicious cyber-attacks within an enterprise security space-based communications architecture to offer a more resilient, protective adaptive framework. Additionally, the machine learning algorithms seek to provide a viable solution for identifying, classifying, and detecting possible intrusions in a highly dynamic environment. Machine learning is also applied to networking strategies to predict congestion before it happens; thereby, preventing bottlenecks within the network. This is especially important for critical, high-value information. A CONgestion Aware Intent-based Routing (CONAIR) architecture that facilitates faster and more reliable data exchanges between end users is proposed. The CONAIR architecture leverages platform and mission information to derive quality of service (QoS) metrics that can be used to support network route optimizations by using a network controller (NC) with machine learning to predict future network behaviors. Finally, the CA, multifunction SDRs and NC subsystems are integrated into a robust architecture on unmanned aerial vehicles (UAVs) to form collaborative cognitive communications systems that are responsive to stressing operating conditions. Through collaborative behaviors and interactions, communications can be optimized. These discriminating technologies support the continued ambition for maturating military communications systems to benefit cooperative interactions and information exchanges between various users in multi-hop, complex networks.
Open Access
Human systems integration of agricultural machinery in developing economy countries: Sudan as a case study
(Colorado State University. Libraries, 2022) Ahmed, Hamza, author; Miller, Erika, advisor; Owiny, James, committee member; Simske, Steve, committee member; Jablonski, Becca, committee member; Herber, Daniel, committee member
Widespread adoption of agricultural machinery for developing economy countries is commonly regarded as a fundamental component of pro-poor growth and sustainable intensification. Mechanized farming can also improve perceptions of farming and mitigate rural-out migration. However, many traditional farmers do not have access to machinery and/or the machinery is cost prohibitive. This study applies the systems engineering approach to identify human-systems integration (HSI) solutions in agricultural practices to more effectively adapt technologies to satisfy traditional farmers' needs. A treatment control study was conducted on 36 farms in Sudan, Africa, over three farming seasons: 2019 (baseline), 2020, and 2021. The treatment group farmers (N = 6) were provided with agricultural machinery (i.e., tractor, cultivator, planter, and harvester), fuel for the machinery, and training to use the machinery. Farmers were interviewed at the beginning of the study and then after each planting and harvesting season during the study. Findings show that the most significant barriers for technology adoption were culture, security, and maintenance costs. However, they also reported that the most significant challenges in their nonmechanized farming practices were related to labor, safety, and profit margins, all of which could be addressed with machinery. Moreover, the results show that all farmers had similar net profits in 2019, when farming without machinery, while mechanized farming yielded significantly higher net profits ($16.61 per acre more in 2020 and $27.10 per acre more in 2021). Farmers also provided needs and rationales of various design options in tractors and attachments. The findings of this dissertation suggest that, despite the initial resistance to using agricultural machinery, the farmers were pleased by their experience after using farming machinery and expressed an even more accepting attitude from their children towards this new farming process. These results demonstrate the importance of developing effective solutions for integrating farming technology into rural farming practices in developing economy countries. More broadly, this study can be used as an HSI framework for identifying design needs and integrating technology into users' lifestyle. The results presented in this dissertation provide a quantified difference between farming with and without machinery, which can provide a financial basis for purchasing and borrowing models, machinery design requirements, and educational value to farmers. Further, the financial values and design requirements can help inform farmers regarding expected costs, returns, and payoffs from tractor adoption. Manufacturers and policymakers can utilize this to promote technology adoption more effectively to farmers in developing economy countries.
Open Access
Investigation of liquid cooling on M9506A high density Keysight AXIE chassis
(Colorado State University. Libraries, 2021) Gilvey, Zachary Howard, author; Bandhauer, Todd M., advisor; Marchese, Anthony, committee member; Simske, Steve, committee member
Forced convection air-cooled heat sinks are the dominant cooling method used in the electronics industry, accounting for 86% of high-density cooling in data centers. However, the continual performance increases of electronics equipment are pushing these air-cooled methods to their limit. Fundamental limitations such as acoustics, cooling power consumption, and heat transfer coefficient are being reached while processor power consumption is steadily rising. In this study, a 4U, 5-slot, high density computing box is studied to determine the maximum heat dissipation in its form factor while operating at an ambient air temperature of 50°C. Two liquid cooling technologies were analyzed in this effort and compared against current state-of-the-art air-cooled systems. A new configuration proposed using return jet impingement with dielectric fluid FC72 directly on the integrated circuit die shows up to a 44% reduction in thermal resistance as compared to current microchannel liquid cooled systems, 0.08 K W-1, vs 0.144 K W-1, respectively. In addition, at high ambient temperatures (~45°C), the radiator of the liquid cooled system accounts for two thirds of the thermal resistance from ambient to junction temperature, indicating that a larger heat exchanger outside the current form factor could increase performance further. The efficiency of the chips was modeled with efficiency predictions based on their junction temperature. On a system level, the model showed that by keeping the chassis at 25°C ambient, the overall power consumption was significantly lower by 500W. Furthermore, the failure rate was accounted for when the chip junction temperature was beyond 75°C. FC72 jet impingement on the die showed the best performance to meet the system cooling requirements and kept the chips below 75°C for the highest ambient temperatures but consumed the most pumping power of all of the fluids and configurations investigated. The configuration with microchannels bypassing TIM 2 showed near the same performance as jet impingement with water on the lid and reduced the junction temperature difference by 5°C when compared to baseline. When the fluid was switched from water to a water glycol 50/50 mixture, an additional thermal resistance of 0.010 K W-1 was recorded at the heat sink level and a higher mass flow rate was required for the GC50/50 heat exchanger to achieve its minimum thermal resistance.
Open Access
Optimizing designer cognition relative to generative design methods
(Colorado State University. Libraries, 2023) Botyarov, Michael, author; Miller, Erika, advisor; Bradley, Thomas, committee member; Forrest, Jeffrey, committee member; Moraes, Marcia, committee member; Simske, Steve, committee member; Radford, Donald, committee member
Generative design is a powerful tool for design creation, particularly for complex engineering problems where a plethora potential design solutions exist. Generative design systems explore the entire solution envelope and present the designer with multiple design alternatives that satisfy specified requirements. Although generative design systems present design solutions to an engineering problem, these systems lack consideration for the human element of the design system. Human cognition, particularly cognitive workload, can be hindered when presented with unparsed generative design system output, thereby reducing the efficiency of the systems engineering life cycle. Therefore, the objective of this dissertation was to develop a structured approach to produce an optimized parsing of spatially different generative design solutions, derived from generative design systems, such that human cognitive performance during the design process is improved. Generative design usability foundation work was conducted to further elaborate on gaps found in the literature in the context of the human component of generative design systems. A generative design application was then created for the purpose of evaluating the research objective. A novel generative design solution space parsing method that leverages the Gower distance matrix and partitioning around medoids (PAM) clustering method was developed and implemented in the generative design application to structurally parse the generative design solution space for the study. The application and associated parsing method were then used by 49 study participants to evaluate performance, workload, and experience during a generative design selection process, given manipulation of both the quantity of designs in the generative design solution space and filtering of parsed subsets of design alternatives. Study data suggests that cognitive workload is lowest when 10 to 100 generative design alternatives are presented for evaluation in the subset of the overall design solution space. However, subjective data indicates a caution when limiting the subset of designs presented, since design selection confidence and satisfaction may be decreased the more limited the design alternative selection becomes. Given these subjective considerations, it is recommended that a generative design solution space consists of 50 to 100 design alternatives, with the proposed clustering parsing method that considers all design alternative variables.
Open Access
Qualitative comparative analysis of software development practices translated from scene to screen using the real-to-real method for inter-industry learning
(Colorado State University. Libraries, 2024) Hawkey, Barry, author; Vans, Marie, advisor; Simske, Steve, committee member; Gallegos, Erika, committee member; Rodgers, Tim, committee member
Many projectized industries, in fields as diverse as healthcare, live theater, and construction, have developed sets of specific project management practices that are consistently associated with success. These practices – assignable activities, tasks, processes, and methods – have been acquired through decades of lessons painfully learned by project teams. Well-known, existing processes allow project teams to capture and disseminate these best practices and lessons learned between projects and across organizations, allowing new teams to benefit from previous efforts. Although overall progress may at times seem fitful, these knowledge-sharing processes have allowed each industry to improve their project management methodologies over time. Unfortunately, the specificity required to make a practice actionable, assignable, and beneficial within the domain of one industry also renders it difficult to apply in another. There is no formal method, or method in widespread use, for the translation of specific project management practices across the boundaries of industry and knowledge domains. As a result, most of the benefits of these learnings - each industry's collective knowledge of best practices – are restricted to their original domain, providing little guidance to project teams in other industries. This research examines several previous attempts to apply project management practices across multiple domains and synthesizes a novel method for such inter-industry learning. The Real-to-Real method presented here begins by identifying potential barriers to project success within a target industry. Next, an industry that has developed different approaches to similar challenges is chosen as a source of inspiration. After holistically examining project management practices within that source industry, a set of evident principles is synthesized through an iterative process of inductive reasoning which explain that industry's approach to project management and these shared challenges. Using these principles as a transformative intermediary, a set of specific practices suitable for the domain of the target industry can then be identified or developed, mirroring or paralleling practices used in the source industry. These practices may lead to improved project outcomes when used in target industry projects that have characteristics similar to those found in the source industry. This method may allow for the translation and practical application of hard-won project management expertise across many projectized industries, potentially improving project outcomes in multiple fields. To provide an illustrative example of the Real-to-Real method in use, the software development industry is selected as an example target, and barriers to project success in that domain are examined. A review of the existing literature finds that the lack of simple, heuristic guidance on tailoring existing practices to better support hedonic requirements, which specify the intended emotional response of the user, may be a significant source of risk within the target industry, although the effect of hedonic requirements on project outcomes has not yet been empirically determined. With this potential source of risk in mind, the film industry is selected as a source of inspiration, as projects there share many similarities with software development projects and must routinely consider hedonic requirements. A holistic evaluation of film production project management practices suggests four evident, explanatory principles guiding that industry's approach to managing projects. This research then identifies and proposes a set of specific practices, suitable for software development projects, which also support or adhere to these same principles, thus mirroring practices used in film production projects. To support these findings, the identified software development practices are situated within existing theory, and potential mechanisms by which they may consistently lead to improved project outcomes when used in projects with high levels of hedonic requirements are discussed. A series of semi-structured interviews with experienced practitioners in the film industry are then conducted to verify an accurate understanding of film production project management practices, the synthesized explanatory principles, and the pairing of each principle to a set of related practices through. Next, a second series of interviews with experienced practitioners in the software development industry is used to verify the selection of software development practices supporting these principles. To empirically validate these findings, and to determine the effect of hedonic requirements on project outcomes, a practitioner survey is then conducted, measuring project success, use of the identified practices, and the level of hedonic requirements in 307 software development project cases in five culturally similar countries. First, the perceived criticality of hedonic requirements is compared to five measures of project success, to determine the impact of such requirements on project outcomes. Then, using Qualitative Comparative Analysis, causal recipes of the identified practices that consistently resulted in project success, across these same measures, are identified for projects with varying levels of hedonic requirements. These results validate the benefits of the identified principles and practices to projects with high levels of hedonic requirements, and provide simple, heuristic guidance to software development project teams on how to quickly and effectively tailor their management practices to better support individual projects based on the criticality of such requirements. This guidance may serve to significantly improve outcomes in software development projects with high levels of hedonic requirements. These results also help to validate the Real-to-Real method of translating management practices across industry and knowledge domains, potentially enabling additional opportunities for valuable inter-industry learning.
Open Access
Relational and technological process concept utilizing a human-in-the-loop-centered methodology for USSOCOM
(Colorado State University. Libraries, 2024) Corl, Kenneth Casselbury, author; Gallegos, Erika, advisor; Bradley, Thomas, committee member; Simske, Steve, committee member; Mumford, Troy, committee member; Crocker, Jerry, committee member
The Department of Defense (DoD) employs broad human factors requirements across various applications, resulting in a universal application of the same standards to a multitude of DoD acquisition systems. In unconventional warfare, specifically within missions conducted by US Special Operations Command (USSOCOM), operators face intensified workloads and domain-specific challenges that current human factors considerations do not adequately address. The objective of this dissertation aims to introduce and validate the Relational and Technological Capstone (RTC), which expands upon existing human factors requirements through both architectural and behavioral diagrams in a well-defined set of methodology-driven process steps. In referencing the system lifecycles as defined by the Defense Acquisition University (DAU) and the International Council on Systems Engineering (INCOSE), the objective is to diversify and enhance the consideration of Human Systems Integration (HSI) requirements in USSOCOM platforms by addressing the unique challenges posed by intensified workloads and domain-specific ontologies. The RTC employs a methodology-driven approach utilizing architectural, behavioral, and parametric diagrams. It integrates with Model Based Systems Engineering (MBSE) and the Systems Modeling Language (SysML) to improve the design of human-system interactions, incorporating a Special Operations Task List and Performance Shaping Factors (PSFs) into aggregated performance values. The results of this dissertation demonstrate the efficacy of RTC within MBSE, showcasing its value through improved design processes and as a foundation for new programs. The RTC can integrate existing models to further benefit customer needs through initiatives like Engineering Change Proposals (ECPs) as well as assist starter models for new programs and projects. The containment tree format aids in developing USSOCOM MBSE and opens possibilities for automation tools as well as an easily transferrable modeling package for future use on all complex systems. Continual use of RTC contributes to the maturity of MBSE models and diagrams, fostering the evolution of a federation-of-models and Program of Record standards. This not only benefits subsequent SOCOM programs and projects but also facilitates the emerging field and methodology of mission engineering to realize and forecast capability gaps before a system reaches the implementation and integration phase. The ultimate goal is to center the RTC around the operator, ensuring man-machine compatibility and optimization throughout special operation acquisitions.
Open Access
Secure CAN logging and data analysis
(Colorado State University. Libraries, 2020) Van, Duy, author; Daily, Jeremy, advisor; Simske, Steve, committee member; Papadopoulos, Christos, committee member; Hayne, Stephen, committee member
Controller Area Network (CAN) communications are an essential element of modern vehicles, particularly heavy trucks. However, CAN protocols are vulnerable from a cybersecurity perspective in that they have no mechanism for authentication or authorization. Attacks on vehicle CAN systems present a risk to driver privacy and possibly driver safety. Therefore, developing new tools and techniques to detect cybersecurity threats within CAN networks is a critical research topic. A key component of this research is compiling a large database of representative CAN data from operational vehicles on the road. This database will be used to develop methods for detecting intrusions or other potential threats. In this paper, an open-source CAN logger was developed that used hardware and software following the industry security standards to securely log and transmit heavy vehicle CAN data. A hardware prototype demonstrated the ability to encrypt data at over 6 Megabits per second (Mbps) and successfully log all data at 100% bus load on a 1 Mbps baud CAN network in a laboratory setting. An AES-128 Cipher Block Chaining (CBC) encryption mode was chosen. A Hardware Security Module (HSM) was used to generate and securely store asymmetric key pairs for cryptographic communication with a third-party cloud database. It also implemented Elliptic-Curve Cryptography (ECC) algorithms to perform key exchange and sign the data for integrity verification. This solution ensures secure data collection and transmission because only encrypted data is ever stored or transmitted, and communication with the third-party cloud server uses shared, asymmetric secret keys as well as Transport Layer Security (TLS).
Open Access
Sustainable recycling of metal machining swarf via spark plasma sintering
(Colorado State University. Libraries, 2021) Sutherland, Alexandra E., author; Ma, Kaka, advisor; Sambur, Justin, committee member; Simske, Steve, committee member
In general, extracting virgin metals from natural resources exerts a significant environmental and economic impact on our earth and society. Production of virgin stainless steels and titanium (Ti) alloys have particularly caused concerns because of the high demands of these two classes of metals across many industries, with low fractions of scraps (less than one-third for steels and one-fourth for Ti alloys) that are currently recirculated back into supply. In addition, the conventional recycling methods for metals require multiple steps and significant energy consumption. With the overarching goal of reducing energy consumption and streamlining recycling practices, the present research investigated the effectiveness of direct reuse of stainless steel swarf and Ti6Al-4V alloy swarf as feedstock for spark plasma sintering (SPS) to make solid bulk samples. The parts made from machining swarf were characterized to tackle material challenges associated with the metal swarf such as irregular shapes and a higher amount of oxygen content. The hypothesis was that while solid bulk parts made from metal swarf would contain undesired pores that degrade mechanical performance, some mechanical properties (e.g., hardness) can be comparable or even outperform the industrial standard counterparts made from virgin materials, because of cold working and grain refinement that occurred to the swarf during machining and the capability of SPS to retain ultrafine microstructures. 304L stainless steel and Ti-6Al-4V (Ti64) alloy swarf were collected directly from machining processes, cleaned, and then consolidated to bulk samples by SPS with or without addition of gas atomized powder. Nanoindentation and Vickers indentation were utilized to evaluate the hardness at two length scales. Ball milling was performed on Ti64 to assess the energy consumption required to effectively convert swarf to varied morphologies. In addition, to provide insight into the macroscale mechanical behavior of the materials made by SPS of recycled swarf, finite element modeling (FEM) was used to predict tensile stress-strain curves and the corresponding stress distributions in the samples. The key findings from my research proved that reuse of austenitic stainless steel chips and Ti64 alloy swarf as feedstock for SPS is an effective and energy efficient approach to recycle metal scraps, compared to the production and use of virgin gas atomized powders, or conventional metal recycling routes. The mechanical performance of the samples made from metal swarf outperformed the relevant industrial standard materials in terms of hardness while the ductility remains a concern due to the presence of pores. Therefore, future work is proposed to continue to address the challenges associated with mechanical performance, including but not limited to, tuning the SPS processing parameters, quantifying an appropriate amount of addition of powder as a sintering aid, and refining the morphology of the swarf by ball milling. It is critical for the health of our planet to always consider the tradeoff between energy consumption and materials performance.