1. :: eBiquity Research Group :: CSEE :: UMBC :: :: :: An Intelligent Broker for Pervasive Context-Aware Systems Harry Chen University of Maryland, Baltimore County December 3, 2004

2. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 2 Outline Introduction (10 mins) The problem, my solution & contributions Related Works (5 mins) Different approaches exist & why mine is better Context Broker Architecture (20 mins) Ontologies, context reasoning & privacy protection Implementations (10 mins) EasyMeeting, CoBrA Demo Toolkit & CTMC Conclusions (7 mins) Lessons learned & future works

3. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 3 Evolution …

4. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 4 Could this be the solution?

5. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 5 The Vision Pervasive Computing: a natural extension of the present human computing life style Using computing technologies will be as natural as using other non-computing technologies (e.g., pen, paper, and cups) Computing services will be something that is available anytime and anywhere.

6. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 6 Pervasive Computing is the Future Pervasive Computing Saves the World

7. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 7 Context-Aware Systems Context-awareness is a key aspect of the intelligent pervasive computing systems Systems that can anticipate users’ needs and act in advance by “understanding” their context A system that knows I am the speaker A system that knows you are the audience A system that knows we are in a conference …

8. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 8 What’s Context? The situational conditions that are associated with a user Location, time, room temperature, lighting conditions, noise level, social activities, user intentions, user beliefs, user roles, personal information, etc.

9. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 9 Research Issues Context Modeling & Reasoning How to represent context, so that it can be processed and reasoned by the computers Knowledge Maintenance & Sharing How to maintain consistent context knowledge and share that information with other systems User Privacy Protection How to let users to control the sharing and the use of their contextual information that is acquired by the hidden sensors

10. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 10 Thesis Statement By developing a broker-centric agent architecture with expressive ontologies, context reasoning procedures, and a policy-based privacy protection mechanism, we can help computing entities to represent and share context, to detect and resolve inconsistent knowledge, and to protect user privacy.

11. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 11 My Research Contributions CoBrA: a broker-centric agent architecture for supporting pervasive context-aware systems Use a Semantic Web language to define ontologies for context modeling and reasoning Use logical inference to interpret context and to detect and resolve inconsistent knowledge Allow users to defined policies to control the sharing of their contextual information

12. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 12 Other Contributions EasyMeeting A smart meeting room prototype that is built on CoBrA; provides context-aware services to speakers and audiences. CoBrA Demo Toolkit An open source software package for demonstrating various aspects of CoBrA CoBrA Text Messaging Commands A text messaging interface for mobile users to interact with a context broker via SMS

13. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 13 Related Work

14. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 14 We’ve Come a Long Way… Since the early 90’s, people have been interested in building context-aware systems Olivetti: Call forwarding & teleporting systems … Xerox PARC: Active map, PARC Tab … Georgia Tech.: Context toolkit, cyberguide … MIT: Office assistant, location-aware information delivery, intelligent room … UC Berkley: Context Fabric UIUC: Gaia HP Labs: Cooltown, CoolAgents … Microsoft: EasyLiving …

15. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 15 A Call-Forwarding System A user has left his office The system forwards the call to a nearby phone The system detects his current location Calls are forwarded to his voice mailbox The system detects the user is in an meeting The phone rings in his office By Roy Want el at. 1992

16. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 16 Types of Context Used The system detects his current location The system detects the user is in an meeting Location Context Activity Context

17. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 17 A Shopping Assistant A user enters a store Turns on his PDA + = + PDA displays the info of a store item As the user wonders around in the store PDA analyzes user’s personal profile PDA recommends store items to the user By Abhaya Asthana el at. 1994

18. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 18 Types of Context Used + = + PDA displays info of the store items PDA analyzes user’s personal profile Location Context Identity Context

19. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 19 Why context-awareness is still a research topic? Where’re they now? The call-forwarding system (1992) The shopping assistant (1994) Microsoft’s EasyLiving (1998) Why haven’t we seen any successful commercialized context-aware systems?

20. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 20 Many pieces are still missing… Demand Wireless Business Sensors Battery Life CPU Power Memory UI Profit Social Acceptance ??? Applications ??? Security ??? Trust ??? … Cross Platform … … … … … … … … … … Context Modeling Context Reasoning Privacy Protection ???

21. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 21 My work adds few more pieces Demand Wireless Business Sensors Battery Life CPU Power Memory UI Profit Social Acceptance ??? Applications ??? Security ??? Trust ??? … Cross Platform … … … … … … … … … … Context Modeling Context Reasoning Privacy Protection ???

22. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 22 Comparing Different Systems Context Modeling Context Reasoning Privacy Protection Gaia/Mist Data Structure & DAML+OIL “Hardwired” procedures Mist Communication Protocol (hides location) Video Streaming App. (Odyssey) Data Structure “Hardwired” procedures N/A MIT Intelligent Room Data Structure Model-based Reasoning N/A XeroxPARC Active Badge Apps Data Structure “Hardwired” procedures N/A Cooltown Museum XML “Hardwired” procedures & SQL N/A MerCeDeS RDF/RDFSRDFS ReasoningN/A Context Broker Architecture OWL OWL & Assumption- based Reasoning User Defined Policy

23. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 23 Shortcomings of the Existing Systems Lacking an adequate representation for modeling context Individual agents are responsible for managing and reasoning their own contextual knowledge Users don’t have control over how their contextual information is shared and used

24. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 24 Context Broker Architecture

25. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 25 The Context Broker Architecture

26. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 26 Key Features of CoBrA Using OWL to define ontologies for context modeling and reasoning Using logical inferences to detect and resolve inconsistent knowledge Using declarative policies to control the sharing of user context

27. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 27 An EasyMeeting Scenario People enter the conference room They “beam” their policy to the broker B B The broker builds the context model Web The broker detects people presence B    Alice’s policy says, “inform my personal agent of my location” A B.. isLocatedIn.. The broker tells her location to her agent A

28. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 28 An EasyMeeting Scenario Her agent informs the broker about her role and intentions + Alice’s policy says, “can share with any agents in the room” A The broker informs the subscribed agents B The Greeting Srv. greets Alice & the others Hello! [xyz] When all expected participants hv arrived OFF DIM The projector agent sets up the slides

29. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 29 CoBrA Components Ontologies Context Reasoning Privacy Protection

30. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 30 Ontologies Context Reasoning Privacy Protection

31. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 31 Ontologies in CoBrA Ontologies are expressed using the Web Ontology Language OWL Ontologies are used to support Context modeling Knowledge sharing Context Reasoning SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications) COBRA-ONT: extends SOUPA; used in the prototypes

32. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 32 KR languages for defining ontologies W3C Recommendations RDF/RDFS -- represents information as N-Triples (subject, predicate, object); supports basic class-subclass & properties. OWL (Web Ontology Language) -- adds more vocab. for describing classes and properties, cardinality, equality, XML datatypes, enumerations etc. http://www.w3.org/2001/sw/ Semantic Web Languages

33. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 33 The Semantic Web Layer Cake we are here “The Semantic Web will globalize KR, just as the WWW globalize hypertext” -- Tim Berners-Lee

34. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 34 Why Ontologies? Why OWL? ontology language service description lang. context model Interop language meta lang (policy) XSLT/XML friendly { PerCom } OWL provided a uniformed language which met many needs in developing a complex pervasive computing system.

35. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 35 The SOUPA Ontology Effort Develop a standard ontology to support pervasive computing applications Helps to facilitate Knowledge sharing & reasoning Service discovery Security and privacy protection Context modeling Reuse/adopt the existing ontologies; define use cases to guide the development process Maintained by the Semantic Web in UbiComp SIG http://pervasive.semanticweb.org

36. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 36 Some Existing Ontologies Time - DAML Time - SOUPA Time Social Network & People - FOAF - SOUPA Person Space & location - CYC space ontology - Region Connection Calculus - SOUPA space & location Device & QoS - FIPA device ontology - SOUPA Device Agent & BDI - MoGATU BDI Ontology - SOUPA Agent & Action Policy - Rei - KAoS policy - Policy in e-Wallet - SOUPA Policy

37. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 37 The SOUPA Ontology

38. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 38 Time Ontology Objective: aims to develop a representative ontology of time that expresses temporal concepts and properties common to any formalization of time Common Usage Describing the start and the end time of an event Reasoning about the temporal order of different events Incorporating time/date XML datatypes into representations that are suitable for temporal reasoning Core constructs (common in DAML Time & SOUPA) Time instant -- point like thing Time interval -- consists of a beginning and an end point XML Datatypes -- date time, duration, time zone etc.

39. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 39 Temporal Ordering Temporal ordering relations Between two time intervals equals overlaps starts & finishes during nonoverlap A.K.A. Allen’s Interval Calculus

40. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 40 A Time Ontology Example An event E1 starts from 2004/09/25 13:00:00 to 2004/09/26 14:30:00

41. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 41 Temporal Ordering (1) The property before is owl:inverseOf the property after

42. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 42 Temporal Ordering (2) The time ontology also includes constructs for expressing temporal order between time instants and intervals

43. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 43 Space & Location Ontology Objective: provide vocabularies for expressing spatial properties and for supporting spatial reasoning Where did this event happen? What rooms are spatially subsumed by this building? Is Bob at work or at home? Is Bob located in the United States? Common Usage: Describe and reason about people’s location context Detect inconsistent information about people’s location Facilitate knowledge sharing between location-aware services Core Constructs Geo-metric: longitude, latitude, altitude (see also OpenGIS) Symbolic: named location (e.g., room, building)

44. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 44 Describing Space Geo-metric Representation George W. Bush is located at 38° 53' 55", -77° 2' 14” (Lat-Long) Symbolic Representation George W. Bush is located at White House Both representations are useful for building location- aware systems Calculate the distance between two points Reason about the proximity of a person’s location

45. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 45 SOUPA Space Ontology The SOUPA space ontology defines vocabularies for describing spatial regions and location properties Covers both symbolic & geo-metric representation Also defines an ontology of the Region Connection Calculus (RCC) for qualitative spatial reasoning ab a b abab ab a is disconnected from b a is externally connected with b a is identical with b a is a tangential proper part of b a partially overlaps b

46. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 46 A Simple UMBC Space Ontology

47. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 47 Where’s Harry?

48. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 48 Detect Inconsistent Information

49. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 49 Policy Ontology Objective: define vocabularies for expressing declarative policies that guide the behavior of computing entities in an open, distributed environment Policies are useful at virtually all levels (OS, networking, data management, security & privacy applications) Policies are less “technical” than programming code Change policies doesn’t require re-compile the source code Common Usage Authorization for services Privacy protection in pervasive computing Policy for guiding team formation and collaboration Core Constructs Positive & negative authorizations & obligations Domain actions

50. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 50 SOUPA Policy Ontology For defining policies to control the performance of actions Each policy defines a set of actions that are either permitted or forbidden (prohibited) Uses Description Logic for policy reasoning It imports other ontologies SOUPA Action ontology SOUPA Time ontology

51. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 51 The SOUPA Policy Ontology

52. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 52 A Policy Example a pol:Policy; pol:policyOf [ a per:Person; per:name "Harry Chen"^^xsd:string ] pol:defaultPolicyMode pol:Conservative;... # Rule 2: all individuals of CLS2 are permitted actions# pol:permits ha:CLS2; ha:CLS2 a :Class; :intersectionOf ( ebact:ShareLocationInfo [ :onProperty act:recipient; :allValuesFrom ebm:EbiquityMember] [ :onProperty act:target; :someValuesFrom ha:MyRestrictedLocationContext ] ). ha:MyRestrictedLocationContext a :Class; :intersectionOf ( loc:LocationContext [ :onProperty loc:boundedWithin; :someValuesFrom ha:foo-a1 ] ). ha:foo-a1 a :Class; :oneOf (ebgeo:ITE210A ebgeo:ITE325B ebgeo:UMBCMainCampus).

53. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 53 Summary CoBrA uses the OWL language to define ontologies for context modeling and knowledge sharing An explicit semantic representation of context is more expressive than the data structure representations used in the past systems Contributions: A standard ontology for pervasive computing applications (SOUPA) Showed the use of Semantic Web technologies in a pervasive computing domain

54. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 54 Context Reasoning Ontologies Privacy Protection

55. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 55 Kinds of Context Reasoning Reasoning for interpreting context e.g., infer location context, user intention & user desire Reasoning for detecting and resolving inconsistent knowledge e.g., conflicting location information about the same person or device

56. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 56 The Use of Rule-Based Systems CoBrA exploits the use of rule-based systems for context reasoning JESS, Prolog & Jena ontology reasoners Different engines are used for different types of context reasoning Some for context interpretation Some for detecting and resolveing inconsistency

57. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 57 Context Reasoning Framework Context Reasoning Framework

58. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 58 Integrating Rule-Based Reasoners

59. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 59 Time Reasoner Two different implementations One in Prolog and the other in Jena Both implement Allen’s Interval Calculus Both support XML date time standards ISO 8601 Date and Time format UTC normalization Jena’s implementation built on Jena’ Generic Rule Reasoner Supports SOUPA Time ontology (RDF/OWL)

60. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 60 Temporal Reasoning Example

61. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 61 Spatial Reasoner Built on the Jena’s Generic Rule Reasoner Supports the SOUPA Space ontology (RDF/OWL) Supports mapping between the “symoblic” and “geo-metric” representations What’s the longitude & latitue of UMBC? Can be extended to support RCC

62. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 62 Supported Spatial Inferences Given geographical space X & Y Infer whether or not X spatially subsumes Y Infer whether or not X is spatially subsumed by Y Infer whether or not X is disconnected from Y Given X & Y with defined latitudes and longitudes Infer whether or not X spatially subsumes Y Infer whether or not X is spatially subsumed by Y Infer whether or not X is disconnected from Y

63. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 63 Meeting Status Reasoner Built on the JESS engine For supporting the EasyMeeting system Infers the context of a scheduled meeting Sensing information is translated from RDF/OWL to Jess rules Reasoning results are mapped back into RDF and stored in the KB

64. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 64 Meeting Context Reasoning

65. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 65 Inconsistency Found! What Should I Do?

66. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 66 Resolve Inconsistent Knowledge Information acquired from different sources may be in conflict Sensor A says, “Harry is in ITE-201” Sensor B says, “Harry is is ITE-338” Q: Who should we believe? Given everything we know about the context, which information is more reliable or less reliable than the others?

67. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 67 Reason over Assumptions Assumptions (defaults) are made when interpreting contexts, and some of which are unreliable Unreliable assumptions cause unreliable conclusions Solution: by disproving unreliable assumptions, we disprove unreliable conclusions that cause conflicts Theorist: an assumption-based reasoning FIPA argumentation ACL (Broker  Broker)

68. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 68 An Example A Broker receives info about people’s location Agent A: using a voice recognition software Agent B: using a Bluetooth cellphone sensor Implicit assumptions about the reliability of the location information The noise level in the room is normal, and which doesn’t affect the accuracy of the voice recognition software (<60 dBA) The co-location relation often holds between a mobile user and the user’s cellphone

69. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 69 Resolve Conflicting Knowledge Agent AContext BrokerAgent B I believe “Harry is in RM201A” I believe “Harry is in RM338” Inconsistency detected What was the room’s noise level? How many missed & outgoing calls on his cellphone? I believe it was “40 dBA” I believe it had “10 missed calls & 0 outgoing calls” Agent A’s info seems to be more reliable

70. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 70 Shortcomings in the Current Approach An explicit assumption model of different agents must be pre-defined for the context broker I.e., what does Agent X believe and why? Changes in other agents’ reasoning implementations will require modifications to be made in the the broker’s assumption model.

71. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 71 Solution: Argumentation Argumentation is the process of weighing various arguments, pro and con, for the truth of an assertion, and arriving at a truth value for the assertion Idea: through argument-based dialogs, the context broker dynamically acquires assumptions from other agents Minimizes the built-in model of other agents’ assumptions

72. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 72 Fatio: an FIPA Argumentation Protocol By Peter McBurney and Simon Parsons (AAMAS 2004)

73. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 73 An Example Two context brokers have distinct beliefs about the presence of a meeting organizer B1 believes the organizer is present B2 believes the organizer is absent To resolve inconsistency, one must convince the other why its belief is more reliable B2 sends messages to question B1 about its belief B1 replies with justifications B2 accepts B1’s assertion if the received justifications are consistent with its own belief

74. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 74 An Argument-based Dialog

75. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 75 Summary CoBrA uses rule-based logical inference to support context reasoning It has a hybrid system architecture that uses different kinds of reasoners (Jena, Jess, Prolog) Contributions: Using ontologies to interprete context An assumption-based approach to resolve inconsistent information Implemented an FIPA argumentation protocol using an assumption-based reasoner (Theorist)

76. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 76 Privacy Protection Ontologies Context Reasoning

77. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 77 Privacy Protection Privacy is about control of information Who has access to what information Related to security but not equal Key issues in a context-aware system Users may be unaware of the hidden sensors and services that share their private information Users can’t hide everything from the system Even if they can, it’s undesirable to hide every bits of information from the system

78. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 78 Policy-Based Privacy Protection CoBrA allows users to define policies to protect their privacy Policies are expressed and reasoned using the SOUPA policy ontology Policy reasoning is built on the classfication feature of a description logic reasoner (Racer)

79. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 79 The SOUPA Policy Ontology

80. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 80 A Policy Example Harry Chen’s policy http://cobra.umbc.edu/ont/2004/05/harrychen-policy Permits Share location info with close friends (Action class) The “recipient ” is restricted to a list of my friend’s URI The “target” is restricted to the “LocationContext” class Forbids Share location info with untrusted agents (Action class) The “recipient” is restricted to a list of untrusted agent URI The “target” is restricted to the “LocationContext” class

81. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 81 Policy Reasoning Behavior

82. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 82 Policy Reasoning Algorithm (highlight) Define an action instant to represent the current knowledge sharing action Check if the user has a defined policy. If not, use a global policy Load the ontology descriptions of the action instant and the policy into Racer Call Racer’s classification function. The action is permitted if the instant is a type of “PermittedAction” class. The action is forbidden if the instant is a type of “ForbiddenAction” class.

83. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 83 Adjusting Information Granularity CoBrA supports information granularity adjustment Instead of hiding all information from the system, only hide the details of a user’s context Extends the policy reasoning algorithm (see Ch. 6)

84. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 84 A Diagram of the Behavior

85. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 85 Adjusting Location Granularity I don’t want others to know the specific room that I’m currently in, but it’s okay to tell others about the my general location Permit to share my location with a granularity >= “State” The broker isLocated(US) => Yes! isLocated(Maryland) => Yes! isLocated(UMBC) => Uncertain.. isLocated(ITE-RM210) => Uncertain..

86. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 86 Adjusting Status Context I don’t want others to know I’m meeting my advisor, but it’s okay to tell others that I’m in a meeting Permit to share my status >= “inMeeting” The broker statusOf(Harry,inMeeting) => Yes! statusOf(Harry,meetingColleagues) => Uncertain statusOf(Harry,meetingAdvisor) => Uncertain

87. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 87 Summary CoBrA uses policies to protect user privacy Policies are expressed using the SOUPA policy ontology; editing policies are similar to editing ontologies Contributions A DL-driven policy reasoning algorithm based on the SOUPA policy ontology Showed the use of information granularity adjustment in policy reasoning

88. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 88 Implementations

89. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 89 Feasibility Demonstration Show the use of CoBrA can help to reduce the efforts in rapidly prototyping pervasive context-aware systems Show CoBrA can be built on the existing FIPA agent and semantic web technologies Show CoBrA testing and monitoring tools can be integrated into the mainstream IDE environment Show new applications can be built on resource-limited devices that exploit CoBrA

90. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 90 Implemented Prototypes EasyMeeting CoBrA Demo Toolkit CEV + ScriptPlay + Context Broker CoBrA Text Messaging Commands

91. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 91 The EasyMeeting Architecture

92. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 92 CoBrA Demo Toolkit http://projects.semwebcentral.org/projects/cobra/ (since April 2004, +400 downloads)

93. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 93 CoBrA Demo Toolkit Built-in script files Examining the temporal reasoning of a context broker (intervals, instants) Examining the spatial reasoning of a context broker (geo-metric, symbolic) Examining the policy reasoning of a context broker (permitted or forbidden actions, location granularity adjustment) Examining a broker’s ability to detect and resolve location inconsistency (assumption-based reasoning)

94. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 94 CoBrA Eclipse Viewer (CEV) For exploring the knowledge and user policies that are stored in the Context Broker; for monitoring the broker’s reasoning process. Inspired by the Java Spider application http://www.javaspider.org

95. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 95 ScriptPlay Agent Testing and debugging the internal behavior of a context via exchanging ACL messages Simulate the roles of sensors, services, or context brokers Script files are defined in XML

96. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 96 CoBrA Text Messaging Commands (CTMC) A text messaging interface for mobile users to interact with the context broker Use SMS to receive and send commands to the broker Similar to Upoc & Dodgeball SMS

97. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 97 CTMC Prototype Works eBiquity weekly meetings that are published on the web; understands the RGB (Research Group In a Box) event ontology Runs 24/7 on ebiquity.orgebiquity.org Supported commands qMEETING qSPEAKER [meeting-id] qINFO [meeting-id] qFOLLOWUP [meeting-id] [email-address]

98. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 98 A Broker’s CTMC Behavior

99. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 99 Conclusions

100. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 100 Conclusions The intelligence of context-aware systems will be limited if they are unable to represent and reason about context Users will abandon the most useful context- aware systems if they are unable to control the sharing of their private information CoBrA is a new architecture that addresses these issues

101. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 101 Lessons Learns CoBrA improves rapid prototyping Context-aware systems needs ontology Logical inference helps to enable context-awareness

102. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 102 Rapid Prototyping using CoBrA In EasyMeeting, the use of contextual knowledge is easy because there is a standard ontology for describing various kinds of information Efforts required to build a context-aware service is reduced because the context broker takes care of context acquistion and reasoning

103. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 103 Ontologies are Important The use of ontologies improves knowledge sharing and representation OWL can express more rich information than programming languages such as Java or C++ OWL provides a uniformed language for knowledge representation, context reasoning, knowledge sharing, and meta-language definitions.

104. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 104 Rule-Based Logical Inference is Better “Hardwired” context interpretation procedures are ridge and difficult to maintain A rule-based approach separates high-level context reasoning from low-level system behaviors Sophisticated reasoning behaviors (e.g., arugmentation) can only be built if the logical inference of context reasoning is explicitly represented

105. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 105 Future Works Scalability issues in real deployment The single-point-of-failure scenario (see Ch.3) Explore probabilistic reasoning to complement rule-based reasoning in CoBrA Understand & improve the time complexity associated with ontology and logical inference (need better tools?) Policy related issues Editing and modifying policies Resolve policy conflicts How to enforce policies after some information has already been shared?

106. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 106 My work adds few more pieces Demand Wireless Business Sensors Battery Life CPU Power Memory UI Profit Social Acceptance ??? Applications ??? Security ??? Trust ??? … Cross Platform … … … … … … … … … … Context Modeling Context Reasoning Privacy Protection ???

107. :: :: :: eBiquity Research Group :: CSEE :: UMBC :: 107 Thank You!