CS595: Foundations of Cyber-Physical Systems (Fall 2011)

List of papers/articles related to this course (recommended reading)

Cyber-Physical Systems



Physical Context


Application Examples

- Environmental Monitoring

- Target Tracking

- Total Ship Computing

- Radar Scheduling

- Personal monitoring

(for Assisted Living)

- Mobiscopes

- World Wide Sensor Web


- Localization Services

- Real-time Scheduling

- Energy and distributed phenomena

Development Challenges

- Conquering Interactive Complexity: Troubleshooting, Debugging, and Avoidance by Design

Programming Paradigms

- Virtual machine, node based, query based, event based, group based, environmentally immersive, and others.

Reading List

Research on cyber-physical systems is driven by the nature of their interaction with the physical world. Such systems are part physical and hence have new attributes that play a major role in system design, development, and execution. These attributes include physical location (of system components), real time, physical energy consumption, external context, and distribution (including the need for composition of global properties from multiple interacting components). Research in cyber-physical systems usually addresses the effects of one or more of these attributes on system design tools, protocols, middleware, operating systems, languages, computing abstractions, simulators, debugging tools, and analytic foundations. This course will follow recent literature on research surrounding the stated attributes.

Observe that there is a large spectrum of cyber-physical applications from the very low end, such as sensor networks, to the very high-end, such as the total shipboard computing environment designed to run battleships. The emphasis of research typically depends on the application class under consideration.

In low-end systems, featuring a multitude of small components, distribution challenges and consequently location-related challenges are dominant. In high-end systems, featuring a smaller number of larger and more expensive components, real-time predictability and reliable composition challenges are dominant. Finally, energy challenges have been addressed in a very broad spectrum of applications from the very low end to the high-end.

Part I: Background (Sensor Networks and CPS Visions)

Background (optional reading):

1.      "Embedded, Everywhere: A Research Agenda for Networked Systems of Embedded Computers,"

Committee on Networked Systems of Embedded Computers, National Academy Press, 2001

2.      Edward A. Lee (UC Berkeley), "Cyber-Physical Systems - Are Computing Foundations Adequate?" presented at the NSF Workshop on Cyber-Physical Systems, October 16, 2006. (Also see Extended Technical Report)

3.      John A. Stankovic et al., Challenges and Opportunities of Physical Computing Systems , IEEE Computer, Nov 2005.

4.      Insup Lee et al., High-Confidence Medical Device Software and Systems , IEEE Computer, April 2006.

Formal Methods

  1. Anthony Hall, Seven Myths of Formal Methods, IEEE Computer, Sep 1990.
  2. Johan Benstsson and Wang Yi, Timed automata: Semantics, Algorithms and Tools
  3. Gerd Behrmann et al, A Tutorial on Uppaal, Nov 2004. (UPPAAL available at www.uuppaal.com)
  4. Anna Philippou and Oleg Sokolsky, Process-Algebraic Analysis of Timing and Schedulability Properties July 12, 2006.

Real-Time Scheduling

  1. Jane W.S. Liu, Real-Time Systems, Prentice Hall, 2000.
  2. Insik Shin and Insup Lee, Compositional Real-Time Scheduling Framework with Periodic Model, ACM TECS, 7(3), April 2008.
  3. C.L. Liu and J.W. Layland, Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment, Journal of the ACM, Vol. 20 No. 1, pp. 40-61, 1973.
  4. Lui Sha et al. "Real Time Scheduling Theory: A Historical Perspective," Journal of Real-time Systems, December 2004.

Feedback in Computer Systems

  1. Tarek Abdelzaher, et al., Introduction to Control Theory And Its Application to Computering Systems, Sigmetrics tutorial, June 2008.
  2. Dan Henriksson, et al., On Dynamic Real-Time Scheduling of Medel Predictive Controllers, CDC, Dec 2002

Part II: Applications

Below we explore examples of cyber-physical applications that motivate emphasis on physical location (the spatial attribute), real time, physical energy consumption, external context, and distribution respectively.

1.  Applications and the Spatial Attribute:

Tracking and Monitoring Applications

Monitoring Examples

1.   Maxim A. Batalin, Mohammad Rahimi, Yan Yu, Duo Liu, Aman Kansal, Gaurav S. Sukhatme, William J. Kaiser, Mark Hansen, Gregory J. Pottie, Mani Srivastava, and Deborah Estrin, "Call and Response: Experiments in Sampling the Environment," ACM Sensys 2004.

2.   Ting Liu, Christopher M. Sadler, Pei Zhang, and Margaret Martonosi, "Implementing Software on Resource-constrained Mobile Sensors: Experiences with Impala and ZebraNet," ACM Mobisys 2004.

Tracking Examples (Update: No summaries are required this week).

1.      J. Liu, M. Chu, J. E. Reich, "Multitarget

 Tracking in Distributed Sensor Networks," IEEE Signal Processing Magazine, Volume 24, Issue 3, May 2007.

2.      Nisheeth Shrivastava, Raghuraman Mudumbai, Upamanyu Madhow, Subhash Suri, "Target Tracking with Binary Proximity Sensors: Fundamental Limits, Minimal Descriptions, and Algorithms," ACM Sensys 2006.

3.   Branislav Kusy, Akos Ledeczi, Xenofon Koutsoukos, "Tracking Mobile Nodes Using RF Doppler Shifts," ACM Sensys 2007.

Optional reading on tracking/monitoring applications:

1.   Javed Aslam, Zack Butler, Florin Constantin, Valentino Crespi, George Cybenko, and Daniela Rus, "Tracking a Moving Object with a Binary Sensor Network," ACM Sensys 2003.

2.   Ning Xu, Sumit Rangwala, Krishna Kant Chintalapudi, Deepak Ganesan, Alan Broad, Ramesh Govindan, Deborah Estrin, "A Wireless Sensor-Network for Structural Monitoring,"  ACM Sensys 2004.

3.   Gyula Simon, Miklos Maroti, Akos Ledeczi, Gyorgy Balogh, Branislav Kusy, Andras Nadas, Gabor Pap, Janos Sallai, Ken Frampton, "Sensor Network-based Countersniper System," ACM Sensys 2004.

4.   L. Gu, D. Jia, P. Vicaire, T. Yan, L. Luo, A. Tirumala, Q. Cao, J. A. Stankovic, T. Abdelzaher, and B. Krogh, "Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments," ACM Sensys 2005.

5.   Leo Selavo, Anthony Wood, Qiuhua Cao, Tamim Sookoor, Hengchang Liu, Aravind Srinivasan, Yafeng Wu, Woochul Kang, John Stankovic, Don Young, John Porter, "Luster: Wireless Sensor Network for Environmental Research," ACM Sensys 2007.

6.   Jude Allred, Ahmad Bilal Hasan, Saroah Panichsakul, William Pisano, Peter Gray, Jyh Huang, Richard Han, Dale Lawrence, Kamran Mohseni, "SensorFlock: An Airborne Wireless Sensor Network of Micro-Air Vehicles," ACM Sensys 2007.  

Applications and the Time Attribute:

Shipboard computing and radar scheduling

Total Ship Computing Environment (TSCE).

1.   TSCE Background (Optional entertaining reading): TSCE is the US Navys futuristic vision of computerized future super-ships. Check out a video clip on the Zumwalt Class Destroyer; the first battleship launching TSCE software. Read a Feb, 2007 press release by Raytheon (the contractor who built the ship) about TSCE launch. A technology review article about the design of this system is available here. Next, see assigned reading.

2.   Yuanfang Zhang, Chenyang Lu, and Christopher Gill, Patrick Lardieri and Gautam Thaker, "Middleware Support for Aperiodic Tasks in Distributed Real-Time Systems," RTAS 2007

3.   Tarek Abdelzaher, Gautam Thaker, Patrick Lardieri, "A Feasible Region for Meeting Aperiodic End-to-end Deadlines in Resource Pipelines," IEEE ICDCS, Tokyo, Japan, March 2004. (No summary requested for papers co-authored by UIUC faculty)

4.    Praveen Jayachandran and Tarek Abdelzaher, "A Delay Composition Theorem for Real-Time Pipelines," Euromicro Conference on Real-Time Systems, Pisa, Italy, July 2007. (No summary requested for papers co-authored by UIUC faculty)

Radar Scheduling

1.   C.-G. Lee, P.-S. Kang, C.-S. Shih, M. Caccamo, L. Sha, "Schedulability Envelope for Real-Time Radar Dwell Scheduling and its Application to Multi-Ship Multi-Radar Systems," Proceedings of International RADAR Conference (RADAR '04), Toulouse, France, October 2004. (No summary requested for papers co-authored by UIUC faculty)

2.   Sathish Gopalakrishnan, Marco Caccamo, Chi-Sheng Shih, Chang-Gun Lee, and Lui Sha, "Finite-horizon scheduling of radar dwells with online template construction," Journal of Real-Time Systems, Volume 33, No. 3, July, 2006. (No summary requested for papers co-authored by UIUC faculty)

Optional reading on distributed real-time applications:

1.   Vinny Cahill, Aline Senart, Doug Schmidt, Stefan Weber, Anthony Harrington, Barbara Hughes, "The Managed Motorway: Real-time Vehicle Scheduling - A Research Agenda," HotMobile 2008.

Applications and the Physical Context Attribute:

Personal and home activity monitoring (e.g., for assisted living)

Personal and home activity monitoring.

1.   Background: The healthcare system in the US might soon collapse due to the flattening of the age pyramid (see nice animation here). This crisis generates a lot of research on smart assisted living facilities to reduce need for care givers for the elderly. Context awareness is a key property of a smart facility. See papers below for typical examples of the current state of research.

2.   E. Munguia Tapia, S. S. Intille, and K. Larson, "Activity recognition in the home setting using simple and ubiquitous sensors," in Proceedings of PERVASIVE 2004, vol. LNCS 3001, A. Ferscha and F. Mattern, Eds. Berlin Heidelberg: Springer-Verlag, 2004, pp. 158-175.

3.   B. Logan, J. Healey, Matthai Philipose, E. Munguia Tapia, and S. Intille, "A long-term evaluation of sensing modalities for activity recognition," in Proceedings of the International Conference on Ubiquitious Computing, vol. LNCS 4717. Berlin Heidelberg: Springer-Verlag, 2007, pp. 483500.

Applications and Distributed Behavior:

Participatory sensing of global phenomena

A vision: Mobiscopes and the World Wide Sensor Web

1.   Tarek Abdelzaher, Yaw Anokwa, Peter Boda, Jeff Burke, Deborah Estrin, Leonidas Guibas, Aman Kansal, Samuel Madden, Jim Reich. "Mobiscopes for Human Spaces." In Pervasive Computing, 2007.

2.   Suman Nath, Jie Liu, and Feng Zhao, "SensorMap for Wide-Area Sensor Webs." IEEE Computer Magazine, 40(7), pp. 90-93, July, 2007.

3.   Bret Hull, Vladimir Bychkovsky, Kevin Chen, Michel Goraczko, Allen Miu, Eugene Shih, Yang Zhang, Hari Balakrishnan, and Samuel Madden, "CarTel: A Distributed Mobile Sensor Computing System," in Proc. ACM SenSys, 2006. Check out the CarTel portal.

4.   Shane B. Eisenman, Emiliano Miluzzo, Nicholas D. Lane, Ronald A. Peterson, Gahng-Seop Ahn, Andrew T. Campbell, "The BikeNet Mobile Sensing System for Cyclist Experience Mapping", Proc. of Fifth ACM Conference on Embedded Networked Sensor Systems (SenSys 2007), Sydney, Australia, Nov. 6-9, 2007.  Check out the BikeNet portal bikeView.

Part III: Services

Below we explore examples of service that enable cyber-physical applications. Such services support application discovery, awareness, or exploitation of physical location (the spatial attribute), real time constraints, physical energy consumption, external context, and distribution respectively.

Services and the Location Attribute:

Localization Services in Sensor Networks:

Selected localization examples.

1.   Ziguo Zhong and Tian He, "MSP: Multi-sequence Positioning of Wireless Sensor Nodes," Proc. of Fifth ACM Conference on Embedded Networked Sensor Systems (SenSys 2007), Sydney, Australia, Nov. 6-9, 2007.  

2.   R. Stoleru, P. Vicaire, T. He, J. A. Stankovic "StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks," In Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys 2006), Boulder, CO, 2006.

Services and the Time Attribute:

Real-time Scheduling:

Foundations and survey..

1.   Liu, C.L., Layland, J.W., "Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment," Journal of the ACM, Vol. 20 No. 1, pp. 40-61, 1973

2.   Lui Sha, Tarek Abdelzaher, Karl-Eric Arzen, Anton Cervin, Theodore Baker, Alan Burns, Giorgio Buttazzo, Marco Caccamo, John Lehoczky, Aloysius K. Mok, "Real Time Scheduling Theory: A Historical Perspective," Journal of Real-time Systems, December 2004. (Warning: Long paper. Will cover over two classes)

Services and Physical Context:

Joint control of time, quality and energy:

The control server - joint scheduling and control quality optimization.

1.   Dan Henriksson, Anton Cervin, Johan Akesson, Karl-Erik Arzen "Feedback Scheduling of Model Predictive Controllers," In Proc. 8th IEEE Real-Time and Embedded Technology and Applications Symposium, San Jose, CA, September 2002.

2.   Anton Cervin, Johan Eker, "Control-Scheduling Codesign of Real-Time Systems: The Control Server Approach," Journal of Embedded Computing, Vol. 1, No. 2, pp. 209--224, 2005.

Energy optimization - joint control of time and energy.

1.   Tibor Horvath, Tarek Abdelzaher, Kevin Skadron, and Xue Liu, "Dynamic Voltage Scaling in Multi-tier Web Servers with End-to-end Delay Control,'' IEEE Transactions on Computers, Vol. 56, No. 4, pp. 444-458, April 2007

2.   Bash, C.B.; Patel, C.D.; Sharma, R.K., "Dynamic thermal management of air cooled data centers," In Proc. 10th Thermal and Thermo-mechanical Phenomena in Electronics Systems, May 2006

Performance Management:

A control theory approach to software performance management.

1.   Tarek F. Abdelzaher, John A. Stankovic, Chenyang Lu, Ronghua Zhang, and Ying Lu, "Feedback Performance Control in Software Services,'' IEEE Control Systems Magazine, Vol 23, No. 3, June 2003.

2.   Tarek Abdelzaher, Yixin Diao, Joseph L. Hellerstein, Chenyang Lu, and Xiaoyun Zhu, "Introduction to Control Theory and its Application to Computing Systems," SIGMETRICS Tutorial, Annapolis, MD, June 2008.

An optimization approach to software performance management.

1.   Chen Lee, John Lehoczky, Raj Rajkumar and Dan Siewiorek "On Quality of Service Optimization with Discrete QoS Options," in Proceedings of the IEEE Real-time Technology and Applications Symposium, June 1999.

Troubleshooting, Debugging, and Avoidance by Design:

Conquering Interactive Complexity

Debugging interactive complexity.

1.   What is normal accident theory? This very interesting book by Charles Perrow explains why some catastrophic accidents are inevitable (and hence "normal") by system design despite taking significant safety precautions, employing redundancy, and following safety protocols correctly:

2.   Charles Perrow, "Normal Accidents: Living with High-risk Technologies," 2nd edition, Princeton University Press, 1999.

3.   K. Marais, N. Dulac and N. Leveson, "Beyond normal accidents and high reliability organizations: the need for an alternative approach to safety in complex systems," MIT ESD Symposium, March 2004.

Designing for simplicity.

1.   Lui Sha, "Using simplicity to control complexity," IEEE Software, Volume 18, Issue 4, July-Aug. 2001.

2.   Tanya Crenshaw, Elsa Gunter, C. L. Robinson, Lui Sha and P. R. Kumar, "The Simplex Reference Model: Limiting Fault-Propagation due to Unreliable Components in Cyber-Physical System Architectures," IEEE Real-time Systems Symposium, Tucson, Arizona, December 2007.

Operating Systems and Middleware. Slides-1 (David Culler on TinyOS). Slides-2 (Ed Lee on Embedded Systems). Slides-3 (Ed Lee on Platforms and Abstractions).

Part IV: Programming Paradigms:

The following are early examples. You are encouraged to follow up on each thread to discover later publications on the subject.

1.   NesC:

David Gay, Phil Levis, Rob von Behren, Matt Welsh, Eric Brewer, and David Culler, The nesC Language: A Holistic Approach to Networked Embedded Systems, PLDI 2003.

1.   Virtual Machines:

Philip Levis and David Culler, Mate: A Tiny Virtual Machine for Sensor Networks, Asplos 2003.

2.   Environmentally Immersive:

Tarek Abdelzaher et al., "EnviroTrack: Towards an Environmental Computing Paradigm for Distributed Sensor Networks," IEEE International Conference on Distributed Computing Systems, Tokyo, Japan, March 2004.

2.   Node-based paradigms:

R. Gummadi, O. Gnawali, and R. Govindan Macro-programming Wireless Sensor Networks using Kairos, DCoSS 2005.

3.   Query-Based Paradigms

Samuel Madden, Michael J. Franklin, and Joseph M. Hellerstein, and Wei Hong, TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks, SigOps 2002.

S. R. Madden, M.J. Franklin, J.M. Hellerstein, W. Hong, TinyDB: An Acquisitional Query Processing System for Sensor Networks, ACM Transactions on Database Systems, vol.30, no.1, March 2005.

4.   Event-Based Paradigms

Elaine Cheong, Judy Liebman, Jie Liu, and Feng Zhao, TinyGALS: A Programming Model for Event-Driven Embedded Systems, SAC 2003.

5.   Group Based Paradigms

Matt Welsh and Geoff Mainland, Programming Sensor Networks Using Abstract Regions, NSDI 2004.

Kamin Whitehouse, Cory Sharp, Eric Brewer, and David Culler, Hood: A Neighborhood Abstraction for Sensor Networks, Mobisys 2004.

6.   State-Centric

Jie Liu, Maurice Chu, Juan Liu, James Reich, and Feng Zhao, State-Centric Programming for Sensor-Actuator Network Systems, Pervasive Computing, 2003.

7.   Bio-inspired Paradigms

Harold Abelson, Don Allen, Daniel Coore, Chris Hanson, George Homsy, Thomas F. Knight, Radhika Nagpal, Erik Rauch, Gerald Jay Sussman, and Ron Weiss, Amorphous Computing, Communications of the ACM, Volume 43 Issue 5, May 2000.


Updated by XiangYang Li, August 2011