The level of intelligence in monitoring & controlling systems are increasing dramatically. The critical issue for an autonomous resilient system is detecting the anomalous behavior through standard patterns to react properly and on time. In cyber-physical systems with the interaction of humans and machines, this will be more complicated. Deceptive alarm is a common dilemma in real systems which could reduce awareness and readiness and accordingly resilience of the system. In this paper, Markov modeling technique is used to predict human behaviors patterns to distinguish between human anomalous behavior and system failure. The data is from the real experience of implementing innovative monitoring system in a five-star hotel which was part of the project of gamification for changing guests’ behavior. The idea was to develop Resilience Supported System to decrease the fault error and alarms and to increase the reliability and resilience of the system.

1. 1
Resilience Supported System for
Innovative Water Monitoring
Technology
Koorosh Aslansefat, Mohammad Hossein Ghodsirad, José Barata and Javad Jassbi
From: CTS-UNINOVA, Universidade Nova de Lisboa (UNL), Portugal
Email: k.aslansefat@campus.fct.unl.pt

2. 2
Table of Content
What we are going to discuss
Introduction
Introduction, Behavior Change Systems, Optishower
Markov Modelling
Models, Assumptions, Basic Concepts
Case Study and Results
Water consumption and related probability density functions, Calculated Probabilities
Resilience-Supported System
Resilience-Supported System and Conclusion
© K. Aslansefat, 2018

3. 3
Introduction
Importance of water
Water Resources
DomesticAgriculture Industry

4. 4
Introduction
Importance of water
Water Resources
DomesticAgriculture Industry
Population
Growth
Draughts
Climate
Change

5. 5
Introduction
Importance of water
Water Resources
DomesticAgriculture Industry
Population
Growth
Draughts
Climate
Change
Increasing
water demand
Decreasing
water supply

6. 6
Introduction
Importance of water
Source: https://www.e-education.psu.edu/drupal6/files/geog030/action/m9_average_daily_water_usage_per_person.png [Accessed: 20.01.2018]

7. 7
Introduction
Three ways to save water
01
02
03
01.
Reduce
02.
Reuse
03.
Recycle

8. 8
Innovative Technology
for Water Metering

9. 9
Optishower
How it works
© K. Aslansefat, 2018

10. 10
Consumption Dashboard
Real Time Consumptions
© K. Aslansefat, 2018

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Consumption Dashboard
Total Consumptions
© K. Aslansefat, 2018

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Markov Modelling

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Problem Statement
The basic Concepts
© K. Aslansefat, 2018

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Problem Statement
The basic Concepts
© K. Aslansefat, 2018
𝑄 𝑁𝑁 = −∞
𝑋𝑡𝑝
𝑞 𝑥 dx , 𝑄 𝑁𝐻 = 𝑋𝑡𝑝
+∞
𝑞 𝑥 dx
𝑃 𝐻𝑁 = −∞
𝑋𝑡𝑝
𝑝 𝑥 dx, 𝑃 𝐻𝐻 = 𝑋𝑡𝑝
+∞
𝑝 𝑥 dx

15. 15
Three-State Markov Model
Typical model
© K. Aslansefat, 2018

16. 16
Case Study and
Results

17. 17
42
Case Study
Our implementation in a Five-star hotel
42 People
Participating
60 Days of Data
Recording
Installing the Sensors
in five Rooms
22 IoT Devices
are Installed
60 5 22
© K. Aslansefat, 2018

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Problem Statement
Real Case Study
© K. Aslansefat, 2018

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Three-State Markov Model
Real Case Study
© K. Aslansefat, 2018

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Resilient Systems

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Resilient Systems
Relationship to Resilient Systems
The resilient system recognize the abnormal behavior and try to recover using potential
capabilities. This will help to reduce the level of the vulnerability of the system and to make sure
that they could work effectively in an uncertain environment.
In this work, we use the experience and the database from the implementation of innovative
technology which was selected by a five-star hotel as a solution for monitoring water and energy.
The main mission was to use gamification and by producing feedback for hotel guests, help to
change the behavior which means here decrease the amount of water and energy used by them.
© K. Aslansefat, 2018

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4) Strange Human Behavior
Resilience-Supported System
Problem Statement
1) Water Leakage
3) Sensors Calibration
© K. Aslansefat, 2018

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Markov-based resilience supported system
© K. Aslansefat, 2018

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Conclusion
What will be the final conclusion
Probabilistic modelling of behavior change system (Optishower) in water and energy
consumption through Markov chain
Based on proposed Markov model, after two days the probability of being in low consumption
will be increased and after ten days we have the steady-state values
A novel idea to have a resilient-supported decision making system in water consumption
monitoring has been proposed.
© K. Aslansefat, 2018

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Acknowledgement
Thanks to
We thank our colleagues from “Temptation
Keeper” company who provided insight and
expertise that greatly assisted the research.
© K. Aslansefat, 2018

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References
Our references
1. Doukas, H., Patlitzianas, K. D., Iatropoulos, K., & Psarras, J. Intelligent Building Energy Management
System Using Rule Sets. Building and Environment, 42(10), 3562-3569. (2007).
2. Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., & Weng, T. Occupancy-driven Energy Management
for Smart Building Automation. Proceedings of the 2nd ACM workshop on embedded sensing systems
for energy-efficiency in building. Zurich, Switzerland. (2010).
3. Han, H., Lee, K., & Soylu, F. Predicting Long-term Outcomes of Educational Interventions Using the
Evolutionary Causal Matrices and Markov Chain Based on Educational Neuroscience. Trends in
Neuroscience and Education, 5(4), 157-165. (2016).
4. Marshall, C., Roberts, B., & Grenn, M. Intelligent Control & Supervision for Autonomous System
Resilience in Uncertain Worlds. 3rd International Conference on Control, Automation and Robotics
(ICCAR). Nagoya, Japan. (2017).
5. Arghandeh, R., von Meier, A., Mehrmanesh, L., & Mili, L. On the Definition of Cyber-physical Resilience
in Power Systems. Renewable and Sustainable Energy Reviews, 58(1), 1060-1069. (2016).
6. Li, M. L., Ramachandran, P., Sahoo, S. K., Adve, S. V., Adve, V. S., & Zhou, Y. Understanding the
Propagation of Hard Errors to Software and Implications for Resilient System Design. ACM SIGARCH
Computer Architecture News, 36(1), 265-276. (2008).
7. Ching, W. K., Fung, E. S., & Ng, M. K. A Multivariate Markov Chain Model for Categorical Data
Sequences and Its Applications in Demand Predictions. IMA Journal of Management Mathematics,
13(3), 187-199. (2002).

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