The document proposes a declarative context description language to specify the behavior of building automation systems. It aims to address the problems with current approaches that are difficult to configure and lack expressivity. The language uses fuzzy logic and temporal logic to define contexts based on sensor values over time. Scenarios are defined as rules with fuzzy conditions and assignments to vary actuator outputs. The proposal is validated through simulations of example scenarios to test functionality. Future work includes improving user personalization, implementing real-world simulations, and extending the language.

1. Specifying the Behaviour of
Building Automation Systems
João Aguiam
September 6th, 2011

2. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
2

3. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
3

4. Motivation
The smart office example
Work Desk
Door
Curtains
Window
Support Table Luminosity Sensor
Luminaries
Motion Sensor
4

5. Motivation
The smart office example
• Maximize energy efficiency
• Control the curtains and luminaries automatically to:
• Reduce the amount of artificial light
• Allow the maximum amount of natural light inside
• Avoid the excessive glare in the working place of the user
• Turn lights off if no one is in the office
• Adjust light level according to user’s task and place
5

6. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
6

7. Problem
How it is done nowadays?
• Crafting behaviour into hardware devices (embedded C programs)
• Difficult to develop
• Lengthy process
• Too far away from the problem domain of BA
• Hardware modules with pre-packaged applications
• Difficult to configure
• Sometimes it is impossible to create the desired behaviour due to
limited expressivity

8. Problem
Statement
• How to specify, in an easy way, the logic behind the
behaviour of a Building Automation System where
the actuators output vary continuously based on
sensors input?
8

9. Problem
Fuzzy controller
• Difficult to specify
• Require great expertise
• Far from the domain level
9

10. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
10

11. Our Proposal
Logical level
Execution level
11

12. Our Proposal
Relation with fuzzy controllers
Rule-base
Fuzzification Inference Defuzzification
Logical level
Execution level
12

13. Our Proposal
Context description language
• Declarative language based on fuzzy logic and
temporal logic
• The degree of truth of a context is defined recursively
on the value of sensors and other contexts
• Some operators reason over a path of past values of
sensor readings and contexts
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14. Our Proposal
Scenario description
• Scenarios are a set of rules which take the form:
if antecedent then consequent
• The antecedent is is a fuzzy condition defined using fuzzy
contexts and fuzzy context operators
• The consequent is an assignment to an actuator which
uses fuzzy consequent operator
• The assignment to an actuator varies with the truth
value of the fuzzy condition
• The defuzification process is a simple weighted average
14

15. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
15

16. Validation
Ideal setting
IDE
IDE
Behaviour
Specification Compiler HW
User
BA Expert
IDE
16

17. Validation
Strategy
1. Illustrative Case Study
2. Excel Model
3. Interactive Simulator
4. Batch Simulator
17

18. Validation
Results and Discussion
Curtains along the day Arriving and leaving
Passage of a cloud in the sky Working at desk
Working at table Walking around
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19. Agenda
• Motivation
• Problem
• Our Proposal
• Validation
• Conclusions
19

20. Conclusions
• We have created an high level declarative language to
specify the behaviour of building automation systems
• Adapted the notion of fuzzy context from Ambient Intelligence
• Created a well defined syntax and semantics that can be
compiled and interpreted
• Introduced the notion of time from the temporal logic with a
new fuzzy Until operator
• Adapted the deffuzification process to a less computational one
• We have validated the idea through diverse simulators to
test the functionality in different scenarios
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21. Conclusions
Future work
• Learning user preferences
• Implementing real world simulation
• Create an interpreted development environment
• Extend the language expressivity
21

22. Thank You
João Aguiam
September 6th, 2011

23. Reserve Slides

24. Context-aware Systems
• Adapts to the surrounding environment
• Environment is abstracted through the notion
of context
• CAS gathers information related to context
and reasons about it to take a certain action
• Context is any information that characterizes
the situation and is relevant for the interaction
between the user and the system
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25. Fuzzy Logic
• Propositions may be partially true or partially false and can
be seen as fuzzy sets
• Fuzzy Sets are represented by a Membership Function (MF)
• In Fuzzy Logic the operation conjunction and disjunction are
calculated through the operator min and max respectively
1
ColdPlace HotPlace
0.8
Membership Degree
0.6 MildPlace
0.4
0.2
0
0 5 10 15 20 25 30 35
X = Temperature
25

26. Fuzzy Logic
• Fuzzy logic systems are based on fuzzy rules
• Fuzzy control systems must result in a single
crisp value to control an object
26

27. Temporal Logic
• Propositions are qualified in terms of time
• Allows the reasoning in a sequence of events
along the time
• Most common operators:
• Next
• Eventually
• Always
• Until
• Releases
27

28. Expert Systems
• Computer program that simulates the
reasoning of a human expert to solve
problems or give advices to the user
• Four topics:
• Knowledge acquisition
• Knowledge representation
• Reasoning control
• Solution explanation
28

29. Fuzzy Context Operator
• Defined in the universe of discourse of a context
• Allow a customized evaluation of a context
• The input of a fuzzy context operator is a context
C and its output a value between 0 and 1
• Defined by a membership function
• Examples are:
No, Little, Some, Enough, Much, Too Much and
Full.
29

30. Fuzzy Context Operator
Examples
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31. Fuzzy Consequent Operator
• Defined in the universe of discourse of an
actuator
• It is specific for a certain type of actuator
• Used in the assignments
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32. Fuzzy Consequent Operator
Examples
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33. Defuzzification
33

34. Mandani Fuzzy Inference System
34

35. Sugeno Fuzzy Model
35

36. Tsukamoto Fuzzy Model
36

37. Curtains Along the Day
Closed
Opened
Hours
37

38. Arriving and Leaving
On
Off
Time
38

39. Passage of a Cloud in the Sky
Time
39

40. Working at Desk
Time
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41. Working at Table
Time
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42. Walking Around
Time
42

43. Limitations
• Single user model
• Absolute values in the actuator assignments
• Set actuators to past values
• Reasoning about time intervals
• Lack of customization for different users
• Validation
• Single case study
• Scalability
• The lack of validation in a real environment
43