Adaptive environment perception in Cyber-Physical Systems Sebastian - - PowerPoint PPT Presentation

▶
adaptive environment perception in cyber physical systems
SMART_READER_LITE
LIVE PREVIEW

Adaptive environment perception in Cyber-Physical Systems Sebastian - - PowerPoint PPT Presentation

Aug 26, 2023 191 likes •358 views

Adaptive environment perception in Cyber-Physical Systems Sebastian Zug, Andr e Dietrich, Christoph Steup and J org Kaiser Dept. of Embedded Smart Systems (ESS) Institute of Distributed Systems University Magdeburg 7th Workshop on

slide-1
SLIDE 1

Adaptive environment perception in Cyber-Physical Systems

Sebastian Zug, Andr´ e Dietrich, Christoph Steup and J¨

  • rg

Kaiser

  • Dept. of Embedded Smart Systems (ESS)

Institute of Distributed Systems University Magdeburg

7th Workshop on Adaptive and Reconfigurable Embedded Systems (APRES 2015) April 13, 2015, Seattle, USA

slide-2
SLIDE 2

Introduction and motivation Concept Evaluation Conclusion

Motivation

Known environments on run-time Statically configured sensor actuators systems Optimized adjustment between sensors and application Focus of the presentation

1

Introduction and motivation Juxtapose Traditional Systems vs. CPS Challenges

2

Concept Adaptive Sensing Controller Sensing optimization

3

Evaluation

4

Conclusion

slide-3
SLIDE 3

Introduction and motivation Concept Evaluation Conclusion

From static controlled systems to CPS

Known environments on run-time Static configured sensor actuators systems Optimized adjustment between sensors and application Occurrence of unexpected situations Adaptive cooperation of smart X Online-adjustment of sensors and applications needed

slide-4
SLIDE 4

Introduction and motivation Concept Evaluation Conclusion

Challenges of adaptive sensor aggregation

Interpretation of unknown data sets Indentification of relevant sensors Transformation, filtering, evaluation, synchronisation of data sets Critical temporal adjustment

application measurment count

S e n s

  • r

p1

  • 1

S e n s

  • r

1

p0

  • p

2 1 2 2 1 2 pp

Sensor 0 Sensor 1 Delay uniform Rayleigh Period 90ms 60ms Offset 35ms 18ms App period pp = 45ms Super period psuper = 360ms

slide-5
SLIDE 5

Introduction and motivation Concept Evaluation Conclusion

Challenges of adaptive sensor aggregation

Interpretation of unknown data sets Indentification of relevant sensors Transformation, filtering, evaluation, synchronisation of data sets Critical temporal adjustment

application measurment count

S e n s

  • r

p1

  • 1

S e n s

  • r

1

p0

  • p

2 1 2 2 1 2 pp

Optimized Schedule

application measurment count

S e n s

  • r

p1

  • 1

S e n s

  • r

1

p0

  • p

1 1 1 2 1 1 1 2 pp

slide-6
SLIDE 6

Introduction and motivation Concept Evaluation Conclusion

Evaluation criteria of a sensor set

Which goals of an application should be monitored? High-level Low-level Precision Minimize variance of the input count Accuracy Maximize minimum input count Reliability Minimize maximal age Minimize average age Minimize mean input uncertainty Minimize failure probability

slide-7
SLIDE 7

Introduction and motivation Concept Evaluation Conclusion

Adaptive Sensing Controller (ASC)

Data sheet evaluation start Static analysis Network monitoring Configu- ration Dynamic Analysis Optimi- zation Application Error state run new sensors available new sensors available no suit- able sensor relevant new sensors specific analysis necessary completed not completed no need for

  • ptimization

not relevant application adjusted valid schedule(s) found processing

  • ffset

detemined A E B C D F G no valid result no valid result

Two level sensing evaluation based on static data sheet information considering dynamic parameters (offsets, delays) additionally

slide-8
SLIDE 8

Introduction and motivation Concept Evaluation Conclusion

Mathematical model - Static analysis I

  • 1. Individual analysis of sensor n

ps < pp count of measurements mmax

  • pp

ps

  • + 1

mmin

  • pp

ps

  • count

probability P(m = mmax)

pp ps −

  • pp

ps

  • P(m = mmin)

1 − pp

ps +

  • pp

ps

  • 2. Calculation of the multi sensor result

0 2 4 6 8 1 0 2 4 6 8 1 0 2 4 6 8 1 * = Pn(m) count Sensor 0 Sensor 1 Convolution

=

k=∞

  • k=−∞

P(k)P(m−k)

slide-9
SLIDE 9

Introduction and motivation Concept Evaluation Conclusion

Mathematical model - Static analysis II

  • 2. Calculation of the multi sensor result

0 2 4 6 8 1 0 2 4 6 8 1 0 2 4 6 8 1 * = Pn(m) count Sensor 0 Sensor 1 Convolution

=

k=∞

  • k=−∞

P(k)P(m−k)

  • 3. Comparism to application demands

at least 6 measurement values per cycle variance of measurement count smaller than x no measurements older than x ms ... A more detailed analysis is necessary!

slide-10
SLIDE 10

Introduction and motivation Concept Evaluation Conclusion

Mathematical model - Dynamic Analysis

  • 1. Individual analysis of sensor n

Offset po Delay related to communication Example: The maximum/minimum age can now calculated by amax = ps − mod(os, gcd(ps, pp)) amin = gcd(ps, pp) − mod(os, gcd(ps, pp))

  • 2. Optimize op

Related to demands of the application Maximum age of the measurement set Variance of the age Uncertainty ...

slide-11
SLIDE 11

Introduction and motivation Concept Evaluation Conclusion

Implementation

Goals Implementation of the ASC as a web-service Evaluation platform for static configured applications

slide-12
SLIDE 12

Introduction and motivation Concept Evaluation Conclusion

Example - Model car tracking

Parameters Car speed approx. 3.2m/s Control algorithm scheduled each ps = 60ms 2 cameras observing with a sensor period ps = 40ms Position measurements are disturbed by Gaussian noise σs = 2cm.

slide-13
SLIDE 13

Introduction and motivation Concept Evaluation Conclusion

Example - Fusion and estimation approach

  • 1. Synchronization of individual sensors by

estimating the position at fusion time ˆ xn(k · tp) = xn(t) + f (k · tp − t) ˆ σn(k · tp) = σs + σv · (k · tp − t)

  • 2. Fusion of all position estimation

ˆ x =

i

  • n=i

ˆ σn2 ·

i

  • n=i

ˆ xn ˆ σn2 ˆ σ2 = 1 i

n=i 1 ˆ σn2

Core idea The age of a data set is mapped on the uncertainty of the position estimation.

slide-14
SLIDE 14

Introduction and motivation Concept Evaluation Conclusion

Example - Results of the optimization

10 20 30 40 50 10 20 Fusion offset op ∆o between cameras 2 2.5 3 3.5 1 2 improvement

  • f ˆ

σ

Optimization goal Chose op in a way, that minimizes the resulting uncertainty ˆ σn

slide-15
SLIDE 15

Introduction and motivation Concept Evaluation Conclusion

Conclusion and future work

Next steps Extended mathematical model considering communication delays Real-World implementation of the car-tracking example Website for testing purposes Future steps Implementation in a more realistic heterogeneous scenario Integration of a network monitoring tool in order to determine the specific data dynamically Development of a concept for configuration switches

slide-16
SLIDE 16

Introduction and motivation Concept Evaluation Conclusion

Data sheet evaluation start Static analysis Network monitoring Configu- ration Dynamic Analysis Optimi- zation Application Error state run new sensors available new sensors available no suit- able sensor relevant new sensors specific analysis necessary completed not completed no need for

  • ptimization

not relevant application adjusted valid schedule(s) found processing

  • ffset

detemined A E B C D F G no valid result no valid result

Thanks for your interest!