Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems
Name:
computers-07-00029-v2.pdf
Size:
3.682Mb
Format:
PDF
Description:
Final Published version
Affiliation
Univ Arizona, Dept Elect & Comp EngnIssue Date
2018-06Keywords
security-driven optimizationheterogeneous multicore systems
mixed cryptographic security model
adaptive system
runtime security optimization
system-level codesign
distributed systems
Metadata
Show full item recordPublisher
MDPICitation
Nam H, Lysecky R. Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems. Computers. 2018; 7(2):29.Journal
COMPUTERSRights
© 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.Collection Information
This 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.Abstract
Embedded 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.Note
Open access journal.ISSN
2073-431XVersion
Final published versionAdditional Links
http://www.mdpi.com/2073-431X/7/2/29ae974a485f413a2113503eed53cd6c53
10.3390/computers7020029
Scopus Count
Collections
Except where otherwise noted, this item's license is described as © 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.