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dc.contributor.authorNam, HyunSuk
dc.contributor.authorLysecky, Roman
dc.date.accessioned2018-09-04T17:04:35Z
dc.date.available2018-09-04T17:04:35Z
dc.date.issued2018-06
dc.identifier.citationNam H, Lysecky R. Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems. Computers. 2018; 7(2):29.en_US
dc.identifier.issn2073-431X
dc.identifier.doi10.3390/computers7020029
dc.identifier.urihttp://hdl.handle.net/10150/628634
dc.description.abstractEmbedded systems continue to execute computational- and memory-intensive applications with vast data sets, dynamic workloads, and dynamic execution characteristics. Adaptive distributed and heterogeneous embedded systems are increasingly critical in supporting dynamic execution requirements. With pervasive network access within these systems, security is a critical design concern that must be considered and optimized within such dynamically adaptive systems. This paper presents a modeling and optimization framework for distributed, heterogeneous embedded systems. A dataflow-based modeling framework for adaptive 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. This level-based security model enables the development of an efficient, multi-objective genetic optimization algorithm to optimize security and energy consumption subject to current application requirements and security policy constraints. The presented methodology is evaluated using a video-based object detection and tracking application and several synthetic benchmarks representing various application types and dynamic execution characteristics. Experimental results demonstrate the benefits of a mixed cryptographic algorithm security model compared to using a single, fixed cryptographic algorithm. Results also highlight how security policy constraints can yield increased security strength and cryptographic diversity for the same energy constraint.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttp://www.mdpi.com/2073-431X/7/2/29en_US
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.subjectsecurity-driven optimizationen_US
dc.subjectheterogeneous multicore systemsen_US
dc.subjectmixed cryptographic security modelen_US
dc.subjectadaptive systemen_US
dc.subjectruntime security optimizationen_US
dc.subjectsystem-level codesignen_US
dc.subjectdistributed systemsen_US
dc.titleMixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systemsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.identifier.journalCOMPUTERSen_US
dc.description.noteOpen access journal.en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleComputers
dc.source.volume7
dc.source.issue2
dc.source.beginpage29
refterms.dateFOA2018-09-04T17:04:36Z


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