1. Providing Robustness in Cyber Physical Systems
Sang Hyuk Son
Dept of Computer Science
University of Virginia
KOCSEA Technical Symposium:
University of Virginia

2. structures, which cause nearly 3,600 fatalities, 18,600 injuries, and
“Each year, there are
an estimated 405,000
fires in residential
structures, which cause
nearly 3,600 fatalities,
18,600 injuries, and
$4.7 billion in property loss.”
U.S. Fire Administration
38% of the homes had fire alarms in perfect condition.
Major cause for smoke alarm failure: disconnected battery (54%)
Leading reason : unwanted activation.
An example of an event we might be interested in detecting is FIRE.
There are two issues that need to be addressed: the high number of false positives as well as the considerable number of false negatives.
Removal for this reason was 8 times as frequent as removal to use batteries for another device.

3. Presentation Overview
Cyber Physical Systems
Robustness: Issues and Challenges
Examples
Wireless communication
Localization
Event service
Summary
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4. Cyber Physical Systems
Systems that have an intimate coupling between the cyber and physical worlds – using sensors and actuators to interact with the physical world
Integrate computing, communication, sensing, and actuation functionalities for monitoring and/or control of entities in the physical world
Require high degree of dependability, availability, predictability, security, and real-time support
High level of variety -- from extremely small and simple devices (e.g., smart dust) to systems of systems.
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5. From Small-Scale Sensors
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6. To Large-Scale Complex Systems
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7. Cyber Physical System Applications
Road and Traffic
Monitoring
Environmental
Monitoring
Battlefield
Surveillance
Infrastructure
Monitoring
Smart
Buildings
Vehicle
Networks
Defining a physical network
Objection: everybody is going to run TCP/IP…
Body
Networks
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8. Cyber Physical Systems
Real-time Systems
Sensor Networks
Cyber-Physical Systems
Embedded Systems
Control Systems
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9. Openness and Robustness in CPS
CPS should support operating in open and dynamic environments
Openness
Correct execution of systems under specific assumptions is not enough
What if assumptions are not satisfied?
Complex physical properties of environments render “individual” solutions brittle
Providing robustness
Need to consider possible dynamics
Need to consider uncertainties and errors
Real-time support is required
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10. Presentation Overview
CPS: A New Frontier
Applications of CPS
Robustness: Issues and Challenges
Examples
Wireless communication
Localization
Event service
Summary
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11. Real Wireless Communication: Irregular and Asymmetric
Irregular Range
of A
Assume B, C, and D are the same distance from A.
Note that this pattern changes over time.
A and B are
asymmetric

12. Impact - Routing Impact on: Path-Reversal technique
Multi-Round technique
Used in AODV, DSR, LAR
RREQ: Route Request
RREP: Route Reply
Route Discovery Using Multi-Round Technique
Impact on Path-Reversal Technique

13. Impact – MAC layer Impact on: Carrier Sense technique
Handshake technique
Used in CSMA, MACA, MACAW, DCF
CSMA: Carrier Sense Multiple Access
MACA: Multiple Access with Collision Avoidance
MACAW: Multiple Access with Collision Avoidance for Wireless
(a) Carrier Sense Technique
(b) Handshake Technique

14. Localization One of the most fundamental problems
A process by which a node determines where it is geographically
Function of many parameters and requirements
Range Based
Determine distances between nodes (range)
Then compute location using geometry
Range Free (connectivity-based)
No need to determine distances directly, instead use hop count
Use average distances between hops

15. Localization via 3 Distance Measurements
Ideal D2 D1 X X
X = anchors or
landmarks or
beacons X D3

16. Localization via N Distance Measurements
Realistic D2 D1 X X
Need to use more than 3 anchors
X = anchors or
landmarks or
beacons X D3

17. Robust Localization Choose range-based algorithm
Choose best / Weighted average
If not localized – try another algorithm
If still not localized by range-based algorithm, try connectivity-based algorithm
All nodes are localized at this point.

18. Evaluation
R. Stoleru, J. Stankovic, and S. H. Son, "On Composability of Localization Protocols for Wireless Sensor Networks,“ IEEE Network Magazine, vol. 22, no. 4, pp , 2008.

19. All nodes are localized
Evaluation
All nodes are localized

20. Robust Event Service Framework
Model development environment
Specification
Analysis
Event detection code
We propose an event service framework that closely follows the structure of an event service.
An event is specified in a formal manner and this specification could also be used for some offline analysis prior or after deployment.
The specification is transformed into code (on the base station) and the code is transferred onto the sensor nodes. The event detection module also collaborates with other modules like timers, synchronization modules, etc.
The input to the detection module are the sensor readings, topology information, etc.
We hope that this architecture will be able to break some of the constraints imposed on the current state of the art.
Sensor node
Event detection module
Other modules
Sensor readings, time, localization, topology …

21. MEDAL Event description language Based on Petri nets
Supports properties specific to sensor networks
Communication
Actuation
Conditional events
Provides real time analysis of the application and its logic

22. Specification using MEDAL

23. Event Semantics Temporal semantics Spatial semantics
MEDAL helps specify temporal constraints such as “when” and “how long”
Tokens are considered related if they have been generated within some predefined time window.
Spatial semantics
Enforces the geographic semantics of the application
Tokens are considered related if they have been generated within some predefined area.

24. Model-based Specification
Fire if the two tokens are present AND they have been generated within 30 seconds AND they have been generated within 5m distance of each other.
Fire if the two tokens are present AND they have been generated within 30 seconds.
Fire if the two tokens are present
Two types of sensors in the network: smoke and temperature. Each node is equipped with either a temperature sensor or a smoke sensor and the temperature nodes make the decision if there is a fire.
Two algorithms: 1) when the temperature goes above some predefined threshold
2) When the temperature and smoke increase faster than some predefined threshold
This model can be used to describe the fire detection system for a skyscraper with 100 floors and hundreds of rooms.
Temporal logic
Spatial logic

25. Potential Problem
Determining the exact event thresholds is a hard task.
The lack of precision of sensor readings increases the complexity.
Example
Suspect fire if both temperature and smoke readings are high
Suppose 50 0C and 50 smoke level are considered high
Is there fire or not if 60 0C but 40 smoke level?
Simple threshold-based approaches could incur many false positives or negatives
A challenge for event detection is that determining the exact event thresholds is an extremely hard task. In addition, the inaccurate sensor readings introduce additional unclearness as to whether an event has occurred or not.

26. Example Cooling is turned on if the temperature > 25°C 25.1°C
One thing is that the values is extremely close to the threshold so how do we choose precision? And if one of the sensors reported an inaccurate reading …
MENTION FALSE POSITIVES AND FALSE NEGATIVES HERE!!!
This is a simple example but if this were a mission critical application we’d want the network to detect the event at the right place and time.
Average
24.95°C

27. Fuzzy Logic for Event Detection
Fuzzy logic based on fuzzy set theory
Originally developed to deal with uncertainties in the physical world
Not binary but continuous membership

28. Structure of Fuzzy Logic
Three main components
Fuzzifier; Inference scheme; Defuzzifier
Fuzzifier
Membership functions for input crisp values
Transfers the input value to fuzzy equivalent
Inference engine
Determines the result based on the rules stored in the rule base
Defuzzifier
Translates the final fuzzy decision into crisp output

29. A Fuzzy Logic System If-Then rules:
Inference Engine
Crisp
input
Crisp
output
Fuzzifier
Defuzzifier
Rule base
A fuzzy logic system has three main components:
Fuzzifier which contains the membership functions for the input variables. It transfers crisp values to their fuzzy equivalents
Inference scheme which determines the result based on the rules stored in the rule base. The rule base contains If-Then rules that determine the relationship between inputs and outputs
Defuzzifier which translated the final fuzzy decision into crisp output.
If-Then rules:
IF temperature is High AND smoke is High THEN Fire confidence is High

30. Crisp vs Fuzzy Values NIST fire experiments
We have ran some simulations on real fire data available through NIST (National Institute of Standards and Technology). In this scenario a mattress was set on fire at time 0 and we have data about 100 min before and 20 min after the ignition. This is the decision of a single node based on its own readings. Smoke and temperature values are considered and the “crisp” thresholds are the ones used in commercial fire detection systems. Same values were used as boundaries between the fuzzy variables.
40 false detections before the fire, affecting the fidelity of the fire detection system

31. Summary
CPS: A number of sensor/actuator nodes to monitor and interact with physical environments/entities, enabling dramatic innovations in a variety of areas
A large number of applications of CPS
Infrastructure monitoring
Surveillance and firefighting
Intelligent highways and automobiles
Smart buildings and power grids
High degree of uncertainty: point solution is not enough
We have just begun -- lots of research issues remain
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32. Thank you!
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