Abstract
Dynamic data-driven applications such as object tracking, surveillance, and other sensing and decision applications are largely dependent on the characteristics of the data streams on which they operate. The underlying models and algorithms of data-driven applications must continually adapt at runtime to changes in data quality and availability to meet both functional and designer-specified performance requirements. Given the dynamic nature of these applications, point solutions produced by traditional design tools cannot be expected to perform adequately across varying execution scenarios. Additionally, the increasing diversity and interdependence of application requirements complicates the design and optimization process. To assist designers of data-driven applications, we present a modeling and optimization framework that enables developers to model an application'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. Further, we show the benefits of using fuzzy logic--based rules over traditional weighted functions for the specification and evaluation of competing high-level metrics in optimization.
- R. Adler, I. Schaefer, M. Trapp, and A. Poetzsch-Heffter. 2010. Component-based modeling and verification of dynamic adaptation in safety-critical embedded systems. ACM Transactions on Embedded Computing Systems 10, 2 (2010), 20.Google Scholar
Digital Library
- D. Allaire, G. Biros, J. Chambers, O. Ghattas, D. Kordonowy, and K. Willcox. 2012. Dynamic data driven methods for self-aware aerospace vehicles. In Proceedings of the International Conference on Computation Science (ICCS’12). 1206--1210.Google Scholar
- R. Bhadani, J. Sprinkle, and M. Bunting. 2018. The CAT vehicle testbed: A simulator with hardware in the loop for autonomous vehicle applications. In Proceedings of the International Workshop on Safe Control of Autonomous Vehicles (SCAV’18). 269.Google Scholar
- V. L. Bageshwar, W. L. Garrard, and R. Rajamani. 2004. Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Technology 53, 5 (2004), 1573--1585.Google Scholar
Cross Ref
- A. Bakshi, V. K. Prasanna, and A. Ledeczi. 2001. MILAN: A model based integrated simulation framework for design of embedded systems. In Proceedings of the 2001 ACM SIGPLAN Workshop on Optimization of Middleware and Distributed Systems. 82--93.Google Scholar
- Y. Bazilevs, A. L. Marsden, F. Lanza di Scalea, A. Majumdar, and M. Tatineni. 2012. Toward a computation steering framework for large-scale composite structures based on continually and dynamically injected sensor data. In Proceedings of the International Conference on Computation Science (ICCS’12). 1149--1158.Google Scholar
- N. Bencomo, J. Whittle, and P. Sawyer. 2010. Requirements reflection: Requirements as runtime entities. In Proceedings of the International Conference on Software Engineering (ICSE’10). 199--202.Google Scholar
- G. Blair, N. Bencomo, and R. B. France. 2009. [email protected]: Guest editors’ introduction. IEE Computer 42 (2009), 22--27.Google Scholar
Digital Library
- Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. 2010. Comparative study of background subtraction algorithms. Journal of Electronic Imaging 19, 3 (2010), 033003.Google Scholar
Cross Ref
- N. Chintalacheruvu and V. Muthukumar. 2012. Video based vehicle detection and its application in intelligent transportation systems. Journal of Transportation Technologies 2, 4 (2012), 305--314.Google Scholar
Cross Ref
- R. De Lemos, H. Giese, H. A. Muller, M. Shaw, J. Andersson, and M. Litoiu. 2013. Software engineering for self-adaptive systems: A second research roadmap. In Software Engineering for Self-Adaptive Systems II. Lecture Notes in Computer Science, Vol. 7475. Springer, 1--32.Google Scholar
- C. Desjardins and B. Chaib-Draa. 2011. Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12, 4 (2011), 1248--1260.Google Scholar
Digital Library
- J. Farrell, M. Okincha, M. Parmar, and B. Wandell. 2010. Using visible SNR (vSNR) to compare the image quality of pixel binning and digital resizing. In Proceedings of SPIE, Volume 7537, Digital Photography VI.Google Scholar
- E. Frew, B. Argrow, A. Houston, C. Weiss, and J. Elston. 2013. An energy-aware airborne dynamic data-driven application system for persistent sampling and surveillance. In Proceedings of the International Conference on Computation Science (ICCS’13). 2008--2017.Google Scholar
- Y. Gedeoglu. 2004. Moving Object Detection Tracking and Classification for Smart Video Surveillance. Master's Thesis. Bilkent University.Google Scholar
- F. Jimenez, E. J. Naranjo, and O. Gomez. 2012. Autonomous manoeuvering systems for collision avoidance on single carriageway roads. Sensors (Basel) 12, 12 (2012), 16498--16521.Google Scholar
Cross Ref
- N. Kehtarnavaz, N. Groswold, K. Miller, and P. Lascoe. 1998. A transportable neural-network approach to autonomous vehicle following. IEEE Transactions on Vehicular Technology 47, 2 (1998), 694--702.Google Scholar
Cross Ref
- A. M. Khaleghi, D. Xu, Z. Wang, M. Li, A. Lobos, J. Liu, and Y. J. Son. 2013. DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs. Expert Systems with Applications 40 (2013), 7168--7183.Google Scholar
Cross Ref
- B. Lamiroy and T. Sun. 2011. Precision and recall without ground truth. In Proceedings of the IAPR International Workshop on Graphics RECognition (GREC’11).Google Scholar
- Q. Lin, Y. Han, and H. Hahn. 2010. Real-time lane departure detection based on extended edge-linking algorithm. In Proceedings of the 2nd International Conference on Computer Research and Development. 725--730.Google Scholar
- A. Lizarraga, J. Sprinkle, and R. Lysecky. 2016. Model-driven optimization of data-adaptable embedded systems. In Proceedings of the IEEE Computer Software and Applications Conference (COMPSAC’16).Google Scholar
- A. Lizarraga, R. Lysecky, S. Lysecky, and A. Gordon-Ross. 2013. Dynamic profiling and fuzzy-logic-based optimization of sensor networks platforms. ACM Transactions on Embedded Computing Systems 13, 3 (2013), Article 51, 29 pages.Google Scholar
Digital Library
- R. Lysecky, N. Sandoval, S. Whitsitt, C. Mackin, and J. Sprinkle. 2013. Efficient reconfiguration methods to enable rapid deployment of runtime reconfigurable systems. In Proceedings of the Asilomar Conference on Signals, Systems, and Computers.Google Scholar
- G. Madey. B. Blake, C. Poellabauer, H. Lu, R. McCune, and Y. Wei. 2012. Applying DDDAS principles to command, control and mission planning for UAV swarms. In Proceedings of the International Conference on Computation Science (ICCS’12). 1177--1186.Google Scholar
- N. Mansurov. 2014. Why Downsampling an Image Reduces Noise. Retrieved December 21, 2019 from https://photographylife.com/why-downsampling-an-image-reduces-noise.Google Scholar
- M. Mathelin, C. Perneel, and M. Acheroy. 1993. Bayesian estimation vs fuzzy logic for heuristic reasoning. In Proceedings of the IEEE International Conference on Fuzzy Systems.Google Scholar
- R. R. McCune and G. R. Madey. 2013. Swarm control of UAVs for cooperative hunting with DDDAS. In Proceedings of the International Conference on Computer Science (ICCS’13). 2537--2544.Google Scholar
- O. Miksik and K. Mikolajczyk. 2012. Evaluation of local detectors and descriptors for fast feature matching. In Proceedings of the International Conference on Pattern Recognition (ICPR’12). 2681--2684.Google Scholar
- S. Neema, T. Bapty, J. Scott, and B. Eames. 2005. Signal Processing Platform: A tool chain for designing high performance signal processing applications. In Proceedings of IEEE SoutheastCon. 302--307.Google Scholar
- S. Neema, J. Sztipanovits, G. Karsai, and K. Butts. Constraint-based design-space exploration and model synthesis. In Embedded Software. Lecture Notes in Computer Science, Vol. 2855. Springer, 290-305.Google Scholar
- W. Pananurak, S. Thanok, and M. Parnichkin. 2008. Adaptive cruise control for an intelligent vehicle. In Proceedings of the IEEE International Conference on Robotics and Biometrics (ROBIO’08). 1794--1799.Google Scholar
- D. H. Parks and S. S. Fels. 2008. Evaluation of background subtraction algorithms with post-processing. In Proceedings of the IEEE 5th International Conference on Advanced Video and Signal Based Surveillance (AVSS’08). 192--199.Google Scholar
- A. Patra, M. Bursik, J. Dehn, M. Jones, M. Pavolonis, E. B. Pitman, T. Singh, P. Singla, and P. Webley. 2012. A DDDAS framework for volcanic ash propagation and hazard analysis. In Proceedings of the International Conference on Computational Science (ICCS’12). 1090--1099.Google Scholar
- L. Peng, D. Lipinski, and K. Mohseni. 2013. Dynamic data driven application system for plume estimation using UAVs. Journal of Intelligent 8 Robotic Systems 74 (2013), 421--436.Google Scholar
Digital Library
- A. Ruiz and G. Juez. 2015. A safe generic adaptation mechanism for smart cars. In Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE’15). 161--171.Google Scholar
- S. Russell and P. Norvig. 2010. Beyond classical search. In Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall, Upper Saddle River, NJ, 125--130.Google Scholar
- N. Sandoval, S. Mackin, S. Whitsitt, R. Lysecky, and J. Sprinkle. 2013. Runtime hardware/software task transition scheduling for data-adaptable embedded systems. In Proceedings of the International Conference on Field-Programmable Technology (FPT’13). 342--345.Google Scholar
- J. Sprinkle, R. Bhadani, S. Cui, and B. Seibold. 2016. Robust control of autonomous vehicle trajectories. In Proceedings of the International Conference on Autonomous Infrastructure, Management, and Security.Google Scholar
- Vanderbilt University. 2005. GME 5 User's Manual. Retrieved December 21, 2019 from https://www.isis.vanderbilt.edu/sites/default/files/GMEUMan.pdf.Google Scholar
- A. Vodacek, J. P. Kerekes, and M. J. Hoffman. 2012. Adaptive optical sensing in an object tracking DDDAS. In Proceedings of the International Conference on Computational Science (ICCS’12). 1159--1166.Google Scholar
- A. Yilmaz, O. Javed, and M. Shah. 2006. Object tracking: A survey. ACM Computing Surveys 38, 4 (2006), Article 13.Google Scholar
- S. Zhou, Y. Jian, J. Xi, J. Gong, G. Xiong, and H. Chen. 2010. A novel lane detection based on geometrical model and Gabor filter. In Proceedings of the 2010 IEEE Intelligent Vehicles Symposium. 59--64.Google Scholar
Index Terms
Automated Model-Based Optimization of Data-Adaptable Embedded Systems
Recommendations
Performance of two Improved Particle Swarm Optimization In Dynamic Optimization Environments
ISDA '06: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02The particle swarm optimization (PSO) was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully in solving various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking ...
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle ...
Dynamic profiling and fuzzy-logic-based optimization of sensor network platforms
The commercialization of sensor-based platforms is facilitating the realization of numerous sensor network applications with diverse application requirements. However, sensor network platforms are becoming increasingly complex to design and optimize due ...






Comments