@article {477,
	title = {Automated Model-based Optimization of Data-Adaptable Embedded Systems},
	journal = {ACM Transactions on Embedded Computing Systems},
	volume = {19},
	year = {2019},
	month = {02/2020},
	pages = {22 pages},
	abstract = {<p><span style="caret-color: rgb(85, 85, 85); color: rgb(85, 85, 85); font-family: \&quot;open sans\&quot;, myriad-pro, sans-serif; font-size: 15.161600112915039px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none; display: inline !important; float: none;">This paper presents a modeling and optimization framework that enables developers to model an application{\textquoteright}s data sources, tasks, and exchanged data tokens; specify application requirements through high-level design metrics and fuzzy logic based optimization rules; and define an estimation framework to automatically optimize the application at runtime. We demonstrate the modeling and optimization process via an example application for video-based vehicle tracking and collision avoidance. We analyze the benefits of runtime optimization by comparing the performance of static point solutions to dynamic solutions over five distinct execution scenarios, showing improvements of up to 74\% for dynamic over static configurations.</span></p>
},
	doi = {10.1145/3372142},
	url = {https://doi.org/10.1145/3372142},
	author = {Adrian Lizarraga and Jonathan Sprinkle and Roman Lysecky}
}
