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Toward robust embedded networks in heavy vehicles - machine learning strategies for fault tolerance

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dc.contributor.authorGhatak, Chandrima, author
dc.contributor.authorRay, Indrakshi, advisor
dc.contributor.authorMalaiya, Yashwant, committee member
dc.contributor.authorKokoszka, Piotr, committee member
dc.date.accessioned2024-09-09T20:51:11Z
dc.date.available2024-09-09T20:51:11Z
dc.date.issued2024
dc.description.abstractIn the domain of critical infrastructure, Medium and Heavy Duty (MHD) vehicles play an integral role in both military and civilian operations. These vehicles are essential for the efficiency and reliability of modern logistics. The operations of modern MHD vehicles are heavily automated through embedded computers called Electronic Control Units (ECUs). These ECUs utilize arrays of sensors to control and optimize various vehicle functions and are critical to maintaining operational effectiveness. In most MHD vehicles, this sensor data is predominantly communicated using the Society of Automotive Engineering's (SAE) J1939 Protocol over Controller Area Networks (CAN) and is vital for the smooth functioning of MHD vehicles. The resilience of these communication networks is especially crucial in adversarial environments where sensor systems are susceptible to disruptions caused by physical (kinetic) or cyber-attacks. This dissertation presents an innovative approach using predictive machine learning algorithms to forecast accurate sensor readings in scenarios where sensor systems become compromised. The study focuses on the SAE J1939 networks in MHD vehicles, utilizing real-world data from a Class 6 Kenworth T270 truck. Three distinct machine-learning methods are explored and evaluated for their effectiveness in predicting missing sensor data. The results demonstrate that these models can nearly accurately predict sensor data, which is essential in preventing the vehicle from entering engine protection or limp modes, thereby extending operational capacity under adverse conditions. Overall, this research highlights the potential of machine learning in enhancing the resilience of networked cyber-physical systems, particularly in MHD vehicles. It underscores the significance of predictive algorithms in maintaining operational feasibility and contributes to the broader discussion on the resilience of critical infrastructure in hostile settings.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierGhatak_colostate_0053N_18490.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239150
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectheavy vehicles
dc.subjectpredictive algorithm
dc.subjectfault tolerance
dc.subjectsecurity
dc.subjectmachine learning
dc.titleToward robust embedded networks in heavy vehicles - machine learning strategies for fault tolerance
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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