Type
Text
Type
Dissertation
Advisor
Doboli, Alex | Wang, Xin | Salman, Emre | Wong, Jennifer
Date
2012-12-01
Keywords
Adaptation Policy Design, Cyber Physical Systems, Distributed Data Modeling, Distributed Variables, Model Parameter Lumping, Timing and resource constraints | Computer engineering
Department
Department of Computer Engineering
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71425
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Cyber-physical systems (CPS) are large, distributed embedded systems that integrate sensing processing, networking and actuation. Developing CPS applications is currently challenging due to the sheer complexity of the related functionality as well as the broad set of constraints and unknowns that must be tackled during operation. Building accurate data representations that model the behavior of the physical environment by establishing important data correlations and capturing physical laws of the monitored entities is critical for dependable decision making under performance and resource constraints. The goal of this thesis is to produce reliable data models starting from raw sensor data under tight resource constraints of the execution platform, while satisfying the timing constraints of the application. This objective was achieved through adaptation policy designs that optimally compute the utilization rates of the available network resources to satisfy the performance requirements of the application while tracking physical entities that can be quasi-static or dynamic in nature. The performance requirements are specified using a declarative, high-level specification notation that correspond to timing, precision and resource constraints of the application. Data model parameters are generated by solving differential equations using data sampled over time and modeling errors occur due to missed data correlations and distributed data lumping of the model parameters. | 203 pages
Recommended Citation
Subramanian, Varun, "Building Distributed Data Models in a Performance-Optimized, Goal-Oriented Optimization Framework for Cyber-Physical Systems" (2012). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 631.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/631