1. A Passive Influence Model for Adapting Environments based on Semantic Preferences MoreLab – Mobility Research Lab http://www.morelab.deusto.es Juan Ignacio Vazquez, Diego López de Ipiña and IñigoSedano CTSB 2006 - International Workshop on Combining Theory and Systems Building in Pervasive Computing (at Pervasive 2006) Dublin, Ireland, 7 May 2006

2. A Passive Influence Model for Adapting Environments based on Semantic Preferences 2 University of Deusto – MoreLabMotivation There is an increasing concern about empowering ubiquitous computing environments with more intelligent capabilities:  To reduce user interaction and annoyance about operating the environment  Reactive spaces: the Ambient Intelligence vision Hypothesis:  Context-awareness seems to be at the heart of this problem.  Automatic environment adaptation to user’s needs would reduce user interaction.  Some form of reasoning would boost up intelligence. SOAM: Smart Object Awareness and Adaptation Model

3. A Passive Influence Model for Adapting Environments based on Semantic Preferences 3 University of Deusto – MoreLab Context information Any environment exposes several kinds of information that can be measured through existing devices: temperature, TV channel, ambient music style and volume, … E = {i 1, i 2, i 3, …, i n } …adopting concrete values at the moment of time t: i 1 (t) = v 1 ; i 2 (t) = v 2 ; i 3 (t) = v 3 ;…; i n (t) = v n E(t) = {i 1 (t), i 2 (t), i 3 (t), …, i n (t) } = {v 1, v 2, v 3, …, v n } In SOAM, the evolution of one concrete kind of environment information depends on its current value, and the influences (constraints) exerted on it: i (t+1) = f ( i (t), C (i, t) ) = f ( v, C (i, t) ) For example: C (temperature, t) = {>23ºC, <27ºC, =25ºC}

4. A Passive Influence Model for Adapting Environments based on Semantic Preferences 4 University of Deusto – MoreLab Context awareness & adaptation So, the state of the environment at time t+1… E(t+1) = { i 1 (t+1), i 2 (t+1), i 3 (t+1), …, i n (t+1) } = {f ( v 1, C(i 1, t) ), f ( v 2, C(i 2, t) ),…, f ( v n, C(i n, t) ) } = F ( E(t), C(E, t) ) …depends on the current state of the environment and all the influences exerted on it at that moment of time. Thus, constraints drive environmental adaptation. In SOAM:  Environment information (context information) is provided by devices  Constraints are processed by devices  Constraints can be expressed at a higher abstraction level in terms of conditional rules: Adaptation Profiles An entity is context-aware when its behaviour is driven by Adaptation Profiles activated under concrete conditions bounded to context information.

5. A Passive Influence Model for Adapting Environments based on Semantic Preferences 5 University of Deusto – MoreLab Passive Influence Within this model, there are no explicit commands or invocations to perform an action on a device like traditional mechanisms (RPC, RMI, SOAP, IIOP, …)  Active Influence. One entity can influence others in the environment by:  Disseminating context information  Disseminating adaptation profiles (desires, suggestions, not orders) It is what we call Passive Influence (Vazquez EUSAI 2004) Example:  As the user enters a hotel room, his PDA disseminates its profile, and the room automatically adapts without explicit intervention (temperature, TV channel configuration).  As the users gets into a rented car, his PDA disseminates its body proportions and suitable adjustments are carried out automatically.

6. A Passive Influence Model for Adapting Environments based on Semantic Preferences 6 University of Deusto – MoreLab Coping with conflict Passive mechanisms perform quite well in conflictive scenarios since there are not conflicting orders, but available information:  Two people with disjoint temperature preferences  the temperature control system decides to set a mid-point between preferred ranges The environment decides how to adapt and resolve the conflict, since it holds the information to take a proper decision. Strategies:  An intermediate value  The first wins  The last wins  Depending on privileges or other attributes  …

7. A Passive Influence Model for Adapting Environments based on Semantic Preferences 7 University of Deusto – MoreLab Issues raised What information to spread and to whom? In which way can elements be influenced? Can reasoning be applied to generate new information? How long does the influence persists? What about data representation, protocols, …?

8. A Passive Influence Model for Adapting Environments based on Semantic Preferences 8 University of Deusto – MoreLab Background research Some Ubicomp architectures apply web technologies for communication & information exchange:  UPnP – Universal Plug and Play  WSAmI: Web Services for Ambient Inteligence Semantic Web technologies (RDF, OWL) are used to represent information concepts and relationships, and generating new knowledge from existing one (reasoning). SW is completely integrated into the web model.  The Web of Knowledge is the next generation web.  The application of SW to UC scenarios has been pioneered in initiatives as Task Computing and CoBrA/SOUPA.

9. A Passive Influence Model for Adapting Environments based on Semantic Preferences 9 University of Deusto – MoreLab The Pervasive Semantic Web Our goal is to create Pervasive Semantic Web environments with devices forming a location- constrained web of knowledge, where they exchange semantic information and perform reasoning, in order to react accordingly to every situation. Context Information is provided by existing devices in the form of RDF using appropriate domain ontologies. Context-Awareness & Adaptation is performed by devices periodically retrieving every others’ provided context information, reasoning upon it and trying to honour active constraints.

10. A Passive Influence Model for Adapting Environments based on Semantic Preferences 10 University of Deusto – MoreLab SOAM Basics SOAM: Smart Objects Awareness and Adaptation Model A Pervasive Semantic Web model for automatic environment adaptation based on user preferences.  Further or manual control is always permitted UPnP devices augmented to exchange semantic information. Basically:  We represent the state of the environment as an RDF Graph (triples)  We represent user preferences as a desired state of the environment (equivalent to RDF triples) under certain conditions (conditional rules)  A special entity, called Orchestrator, tries to change the environment state based on devices’ declared capabilities.

11. A Passive Influence Model for Adapting Environments based on Semantic Preferences 11 University of Deusto – MoreLab SOAM Architecture Messages:  Adaptation Profiles Adaptation Profiles  Capabilities Capabilities  Context Information Context Information  Constraints Constraints Entities  Adaptation User-Agent  Smobjects  Orchestrator Phases: 1. Discovery 2. Adaptation-Profiles Injection 3. Capabilities retrieval 4. Context Information Retrieval 5. Reasoning (DL, domain rules) 6. Constraints injection

12. A Passive Influence Model for Adapting Environments based on Semantic Preferences 12 University of Deusto – MoreLabExamples Example 1:  AP: “I would like to listen classical music when I am working with my laptop”  HiFi system’s capabilities: “I can operate on the music domain”  Laptop’s capabilities: “I can perceive who is working with me”  Orchestrator: Retrieves Context Information (from Laptop) If preconditions are true Send to the HiFi system: “play classical music” Example 2:  AP: “If I am at location x of type HotelRoom, I would like x’s temperature to be 24ºC”  Room: I can perceive my type  Location Control System: I can perceive whether the guest is in.  Temperature control system’s capabilities: I can perceive the temperature of the present location, I can operate the temperature of the present location  Orchestrator: Retrieves Context Information (from the Room and the Location control system) If preconditions are true Send to the TempControlSystem: “set temperature to 24ºC”

13. A Passive Influence Model for Adapting Environments based on Semantic Preferences 13 University of Deusto – MoreLabResults Smobjects HW: ARM7-based 55 MHz with μClinux and a Java VM platform, simulating devices (Hi-Fi, TV, lights, temperature control, microphone, …). Small form factor: 7cm x 2cm. Orchestrator: Pentium-M 1.86 GHz, Jena2 API Recently migrated to a distributed choreograhic model without central orchestration

14. A Passive Influence Model for Adapting Environments based on Semantic Preferences 14 University of Deusto – MoreLab Conclusions and Contact Theory vs Implementation:  Semantic Web was a fine selection  Platform limitations: no orchestrator at every device, power consumption MoreLab – Mobility Research Lab at the University of Deusto: http://www.morelab.deusto.eshttp://www.morelab.deusto.es My email: ivazquez@eside.deusto.es

15. A Passive Influence Model for Adapting Environments based on Semantic Preferences 15 University of Deusto – MoreLab Examples – Adaptation Profile 3

16. A Passive Influence Model for Adapting Environments based on Semantic Preferences 16 University of Deusto – MoreLab Examples – Capabilities

17. A Passive Influence Model for Adapting Environments based on Semantic Preferences 17 University of Deusto – MoreLab Examples – Context Information <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://www.awareit.com/onto/examples/example1#"> 8

18. A Passive Influence Model for Adapting Environments based on Semantic Preferences 18 University of Deusto – MoreLab Examples - Constraints 3