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dc.contributor.advisorLysecky, Romanen
dc.contributor.authorNam, HyunSuk
dc.creatorNam, HyunSuken
dc.date.accessioned2017-06-21T15:30:22Z
dc.date.available2017-06-21T15:30:22Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10150/624287
dc.description.abstractDistributed heterogeneous embedded systems are increasingly prevalent in numerous applications, including automotive, avionics, smart and connected cities, Internet of Things, etc. With pervasive network access within these systems, security is a critical design concern. This dissertation presents a modeling and optimization framework for distributed, reconfigurable, and heterogeneous embedded systems. Distributed embedded systems consist of numerous interconnected embedded devices, each composed of different computing resources, such single core processors, asymmetric multicore processors, field-programmable gate arrays (FPGAs), and various combinations thereof. A dataflow-based modeling framework for streaming applications integrates models for computational latency, mixed cryptographic implementations for inter-task and intra task communication, security levels, communication latency, and power consumption. For the security model, we present a level-based modeling of cryptographic algorithms using mixed cryptographic implementations, including both symmetric and asymmetric implementations. We utilize a multi-objective genetic optimization algorithm to optimize security and energy consumption subject to latency and minimum security level constraints. The presented methodology is evaluated using a video-based object detection and tracking application and several synthetic benchmarks representing various application types. Experimental results for these design and optimization frameworks demonstrate the benefits of mixed cryptographic algorithm security model compared to single cryptographic algorithm alternatives. We further consider several distributed heterogeneous embedded systems architectures.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.subjectCodesignen
dc.subjectDistributed Embedded Systemsen
dc.subjectGenetic Algorithmen
dc.subjectMixed Cryptographyen
dc.titleSecurity-driven Design Optimization of Mixed Cryptographic Implementations in Distributed, Reconfigurable, and Heterogeneous Embedded Systemsen_US
dc.typetexten
dc.typeElectronic Dissertationen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.leveldoctoralen
dc.contributor.committeememberLysecky, Romanen
dc.contributor.committeememberAkoglu, Alien
dc.contributor.committeememberRoveda, Janet Meilingen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineElectrical & Computer Engineeringen
thesis.degree.namePh.D.en
refterms.dateFOA2018-06-26T00:06:03Z
html.description.abstractDistributed heterogeneous embedded systems are increasingly prevalent in numerous applications, including automotive, avionics, smart and connected cities, Internet of Things, etc. With pervasive network access within these systems, security is a critical design concern. This dissertation presents a modeling and optimization framework for distributed, reconfigurable, and heterogeneous embedded systems. Distributed embedded systems consist of numerous interconnected embedded devices, each composed of different computing resources, such single core processors, asymmetric multicore processors, field-programmable gate arrays (FPGAs), and various combinations thereof. A dataflow-based modeling framework for streaming applications integrates models for computational latency, mixed cryptographic implementations for inter-task and intra task communication, security levels, communication latency, and power consumption. For the security model, we present a level-based modeling of cryptographic algorithms using mixed cryptographic implementations, including both symmetric and asymmetric implementations. We utilize a multi-objective genetic optimization algorithm to optimize security and energy consumption subject to latency and minimum security level constraints. The presented methodology is evaluated using a video-based object detection and tracking application and several synthetic benchmarks representing various application types. Experimental results for these design and optimization frameworks demonstrate the benefits of mixed cryptographic algorithm security model compared to single cryptographic algorithm alternatives. We further consider several distributed heterogeneous embedded systems architectures.


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