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Outline Motivation Context and Context-aware Computing

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0 Context-aware Computing: Basic Concepts
金仲達教授 清華大學資訊系統與應用研究所 九十三學年度第一學期

1 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

2 Sources “Out of Context: Computer Systems That Adapt to, and Learn from, Context,” H. Lieberman, T. Selker, MIT “A Survey of Context-Aware Mobile Computing Research,” by G. Chen, D. Kotz, Dartmouth College “Context-Aware Applications Survey,” M. Korkea-aho, Helsinki University of Technology Slides from Jason I. Hong, Group for User Interface Research, U. of California at Berkeley Pervasive Computing

3 Motivation Modern computers are divorced from reality
Unaware of who, where, and what around them Leads to mismatch Computers have extremely limited input Aware of explicit input only Can take a lot of effort to do simple things Context-Aware Computing Making computers more aware of the physical and social worlds we live in Breaking computers out of the box Mismatch between how people do things and what today's computers can offer Screen saver activates at wrong times Cell phones interrupt us at bad times Pervasive Computing

4 Traditional View of Computer Systems
Context independent: acts exactly the same Computer System input output Human in the loop Pervasive Computing

5 From Abstraction to Context Sensitivity
Traditional black box view comes from the desire for abstraction This is based on several assumptions: Explicit input/output: slow, intrusive, requiring user attention Sequential input-output loop Move away from the black box model and into context-sensitivity human out-of-the-loop (as much as possible) reduce explicit interaction (as much as possible) Pervasive Computing

6 Context as Implicit Input/Output
explicit input Context-Aware System explicit output Context: state of the user state of the physical environment state of the computing system history of user-computer interaction ... Pervasive Computing

7 Context-Aware Computing
Let computer systems sense automatically, remember history, and adapt to changing situations Reduced explicit interaction, more responsive Need to draw a boundary around the system under consideration To define “explicit” and “implicit” Pervasive Computing

8 Why Context-Aware Computing?
Existing Examples Context Types Human Concern Room Activity Auto Lights On / Off Convenience Context-Awareness isn't new, it's fundamental to how we build things (tools and applications) Personal Identity & Time File Systems Finding Info Time Calendar Reminders Memory Room Activity Smoke Alarm Safety Object Identity Barcode Scanners Efficiency Pervasive Computing

9 Why Context-Aware Computing?
Existing Examples Potential Examples Context Types Human Concern Auto Lights On / Off Auto Cell Phone Off In Meetings Identity Time Location Proximity Activity History Activity Convenience Here are some potential context-aware applications enabled by these new technologies Other concerns Rapid decision making Health for elderly and for young Multimodal interaction and disambiguation Efficiency Safety Tag Photos File Systems Activity Finding Info Calendar Reminders Proximal Reminders Identity Memory Smoke Alarm Health Alert Identity & Time Safety Barcode Scanners Service Fleet Dispatching Time Efficiency Pervasive Computing

10 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

11 Definition of Context (1/3)
Schilit divides context into three categories: Computing context User context Physical context Time is also important and natural context Time context => context history Pervasive Computing

12 Definition of Context (2/3)
Schmidt et al.: “knowledge about user’s and IT device’s state, including surroundings, situation, and to a less extent, location” Dey: “any information that can be used to characterize the situation of an entity” Entity: person, place, object that is considered relevant to the interaction between a user and an application Pervasive Computing

13 Definition of Context (3/3)
Kotz: “the set of environmental states and settings that either determines an application’s behavior or in which an application event occurs and is interesting to the user” Active context: influences behavior of an application Passive context: relevant to the application, but not critical Pervasive Computing

14 Examples of Context Identity Spatial: location, orientation, speed
Temporal: date, time of day, season Environmental: temperature, light, noise Social: people nearby, activity, calendar Resources: nearby, availability Physiological: blood pressure, heart rate, tone of voice Pervasive Computing

15 Context-aware Computing (1/3)
Pascoe: taxonomy of context-aware features contextual sensing context adaptation contextual resource discovery contextual augmentation (associating digital data with user’s context) Pervasive Computing

16 Context-aware Computing (2/3)
Dey: context-aware features presentation of information/services to a user according to current context automatic execution of a service when in a certain context tagging context to information for later retrieval Pervasive Computing

17 Context-aware Computing (3/3)
Kotz: Active context awareness - An application automatically adapts to discovered context, by changing the application’s behavior Passive context awareness - An application presents the new or updated context to an interested user or makes the context persistent for the user to retrieve later. Pervasive Computing

18 Context-Aware and Pervasive
What is the relationship between context-aware computing and pervasive computing? Pervasive Computing

19 Outline Motivation Context and Context-aware Computing
Context-aware Applications Taxonomy Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

20 Examples of Context-awareness
垃圾郵件過濾 汽車恆溫系統 會議記錄 開會中關閉手機 家裡的老人家跌倒了,叫救護車! Situation/ high-level contexts Pervasive Computing

21 Active Badges Badges emit infrared signals Teleport Phone forwarding
Gives rough location + ID Teleport Redirect screen output from "home" computer to nearby computer Phone forwarding Automatically forward phone calls to nearest phone Active Badge Olivetti / AT&T Hopper, Harter, et al Pervasive Computing

22 Active Badges (cont’d)
Interface follow-me (location) Pervasive Computing

23 ParcTabs Active badge + wireless ParcTabs Rough location + ID
Showing information of the room the user in Help find resources Show all files in a directory when enter a room Locate others Different control choices in different rooms (location, time, nearby devices, file system state) ParcTabs Xerox PARC Want, Schilit, et al Pervasive Computing

24 Auto-diaries and Proximate Selection
Forget-me-not system Pervasive Computing

25 In/Out Board (Georgia Tech)
Context: identity by FRID, time Pervasive Computing

26 DUMMBO (Georgia Tech) Dynamic Ubiquitous Mobile Meeting Board:
Digitizing whiteboard to capture and access informal and spontaneous meetings Capture ink written to and erased from whiteboard, and audio discussion Activated when two or more people gathered around Context: ID, time, location of whiteboard Pervasive Computing

27 Cyberguide GPS or infrared tracking Display location on screen
Fairly precise location Display location on screen Predefined points of interest Automatically pop up if nearby Travel journal Keep log of places seen and photographs taken Context: location, time The most often created context-aware application Cyberguide Georgia Tech Abowd et al Pervasive Computing

28 Cyberguide (cont’d) Pervasive Computing

29 Enhanced PDA Voice memo Portrait/Landscape Tilt scrolling
Hold like phone near mouth to start recording Portrait/Landscape Physically rotate screen Tilt scrolling Tilt instead of scrollbars Power management Turn on if being held and tilted Microsoft Research Hinckley et al Pervasive Computing

30 GUIDE (University of Lancaster)
Context: location through WLAN, user preference Pervasive Computing

31 Fieldwork University of Kent at Canterbury: archeological assistant
giraffe observation rhino identification (location through PalmPilot, GPS; time) Location dependent notes through StickPlate, StickEdit, StickMap Pervasive Computing

32 Memory Aids Forget-Me-Not: Rank Xerox StartleCam: MIT Media lab.
ParcTab recording where its user is, who they are with, whom they phone, etc. in a database for later retrieval StartleCam: MIT Media lab. Skin conductivity sensor triggers taking of images and transmitting to remote server Pervasive Computing

33 Other Applications Shopping assistant (location)
Smart floor, active floor Office assistant from MIT Media Lab. (activity, schedule) Pervasive Computing

34 Summary: A Rough Taxonomy of Context-Aware Apps
Triggers Metadata Tagging Reconfiguration and Streamlining Input specification Presentation "Who is that person sitting next to Uncle Ralph?" "Where on earth was that picture taken?" "Was this a photo from my second wedding? Or my third?" Pervasive Computing

35 A Rough Taxonomy of Context-Aware Apps
Triggers On X do Y "Notify doctor and nearby ambulances if serious health problem detected" "Remind me to talk to Chris about user studies next time I see him" "Who is that person sitting next to Uncle Ralph?" "Where on earth was that picture taken?" "Was this a photo from my second wedding? Or my third?" Pervasive Computing

36 A Rough Taxonomy of Context-Aware Apps
Metadata Tagging "Where was this picture taken?" "Find all notes taken while Mae was talking" Memory prosthesis Stick-e notes: University of Kent Stick-e note: attaching notes to a context, later trigger the node when context occurs again Programming environment based on stick-e: Triggering, execution, and sensor components "Who is that person sitting next to Uncle Ralph?" "Where on earth was that picture taken?" "Was this a photo from my second wedding? Or my third?" Pervasive Computing

37 A Rough Taxonomy of Context-Aware Apps
Reconfiguration and Streamlining Telephone forwarding and Teleport Turn off cell phone in theaters Automatically adjust brightness / volume Automatic file pre-caching Select modes in multimodal interaction Multimedia / Bandwidth adaptation "Who is that person sitting next to Uncle Ralph?" "Where on earth was that picture taken?" "Was this a photo from my second wedding? Or my third?" Pervasive Computing

38 A Rough Taxonomy of Context-Aware Apps
Input specification Send mail only to people in building now Print to nearest printer "Find gas stations nearest me" Presentation of plain contexts Current location Idle? Currently in? Contextual info about objects Proximate selection Pervasive Computing

39 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

40 Design Process of Typical Context-aware Applications
1. Specification 2. Acquisition and Representation 3. Delivery/Distribution 4. Reception and Storage 5. Action (the application) Pervasive Computing

41 Design Process: Specification
Context to use Context behaviors to perform Key step in design process: problem specification Pervasive Computing

42 Design Process: Acquisition
Install relevant sensors Sensors: infrastructure or personal artifacts Where to sense? How often to update and report? Context representation Store context Pervasive Computing

43 Design Process: Delivery/Distribution
Contexts typically captured remotely from applications at different time Context captured in sensor-rich environment or device may need to serve multiple applications => Need to deliver and distribute context to multiple, remote applications Infrastructure or middleware support App/network-level delivery/routing models and transport mechanism Pervasive Computing

44 Design Process: Reception
Application locates relevant sensors/contexts Service discovery Requests contexts via queries, polls, notifications Query language, event-notification mechanism How often to request? Additional interpretation/abstraction/processing Collection, aggregation, filtering, correlation, fusion,... Pervasive Computing

45 Design Process: Action
Combine received contexts with previous contexts and system/application states for further analysis Perform actions based on the analysis results May treat context collection/processing as a separate service Pervasive Computing

46 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

47 Sensing the Context (1/3)
Location: Outdoors: GPS Indoors: IR, RF, ultrasonic, camera (cellular and non-cellular) Hybrid: IEEE , Mobile-IP Issues: Heterogeneous sensors with uncertainty and conflicts (sensor fusion) Data vs sensor networks Making mobile devices location-aware Pervasive Computing

48 Sensing the Context (2/3)
Low-level contexts beyond location Time: time-of-day (with calendar) Nearby objects Network bandwidth Orientation Others: photodiode (light), accelerometer (tilt, vibration), microphone, sensors for temperature, pressure, gas, etc. Issue: sensors in mobile devices or infrastructure => direct vs. indirect awareness Pervasive Computing

49 Sensing the Context (3/3)
High-level contexts: user’s activity Camera technology and image processing Consult calendar for what user is to do Combine low-level sensors, e.g., using rules How about emotional contexts? Context changes: subscription-notification Polling rate? Pervasive Computing

50 Quality of Contexts What context is important? Always and in different situations? Quality: Coverage, resolution, accuracy, confidence, reliability, frequency, timeliness Self-contained vs. infrastructure-supported PDA doesn't need location sensors if it can ask nearby sensors to approximate Need standards for sharing components? Pervasive Computing

51 Modeling Contexts Location model:
Symbolic: Active Badge, symbols Geometric: GPS, coordinates Environmental model: relationship between ... Locations: hierarchical, containment, distance People: friend, family, hierarchy Devices? Context representation: Key-value pairs, tagged encoding Object-oriented model: contexts as object states Logic-based model: facts in rule-based system Pervasive Computing

52 Context Specification
Need to express context about and relationships between People, Places, Things Predicates Identity (Who is…? What is…? Is with…?) Location (Near? Nearest? Distance? Path?) Activity (Is busy? Is in meeting? Current task?) Time (In past? In present? In future? On date?) Some of this vocabulary done by Schilit Implicitly encoded in his APIs One goal is to extend his work in spec language Another is to make it extensible for future context types Pervasive Computing

53 Context Specification
Common parameters Max number of results wanted Return name Return data type (e.g. String, List, Table) Minimal probability of correctness desired Relevant sensor input requestor has Event parameters Event rate (e.g. at most 1 event per second) Event callback (e.g. RPC, socket port) Max number of events desired Granularity of change (e.g. 1 meter) Pervasive Computing

54 Context Interpretation
Sophisticated applications require higher level forms of context Fusion Ambiguity: Sensors not 100% reliable, e.g. confidence value Precision / Accuracy / Granularity Different ways to deal: Improve inference Probability/fuzzy model Bring the user into the loop Pervasive Computing

55 System Issues (1/2) Programming model Interoperability Evaluation
Programming the physical world Unreliable sensors, recognition algorithms, plus standard distributed computing issues Interoperability Sensors, services, and devices Useless if everyone has proprietary / custom systems Need standard data formats, protocols, and frameworks Varying capabilities of sensors, services, and devices Evaluation Pervasive Computing

56 System Issues (2/2) May need a middleware layer to decouple applications and context sensing Collect raw context, translate to application-understandable format, disseminate it Centralized context server Distributed architecture Pervasive Computing

57 Intelligence Who is smart? User or system or both
Who makes the decisions on what actions to take? Tradeoff between user cognitive load and effort to make system “smart” Pervasive Computing

58 People Issues Avoiding embarrassing situations
Active Badges + bathrooms Inconvenient phone forwarding Avoiding dangerous situations Need to take into consideration cost of mistake Smoke alarms when cooking Lights that turn off when you're still there Woman locked in "smart toilet stall" Will adding more context really help here? Pervasive Computing

59 People Issues Making it predictable and understandable Privacy
Setting preferences "I want my cell phone to ring except in theaters and when I'm in a meeting unless…" Why the heck did it do that? Privacy What does the computer know about me? What do others know about me? Capturing/collecting lots of information about people, places and devices People uncomfortable when don’t know what is being collected and how it’s used Pervasive Computing

60 Killer Applications? Need something to focus and drive the research
Need something to put in the hands of real people Business model: how to make money from it? Pervasive Computing

61 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Handling Multiple Contexts Context Modeling Context Programming Summary Pervasive Computing

62 Manipulating Multiple Contexts
Source: “Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts” H.W. Gellersen, A. Schmidt, M. Beigl Lancaster University and University of Karlsruhe Situation perceived sensor(s) Context Pervasive Computing

63 Direct/Indirect Context-Awareness
Situation device A Infrastructure device B Context communicate perceived sensor(s) 獨立於infrastructure,以及測量溫度的問題 device C device A Situation S(s) Built-in sensors, context processing device B perceived S(s) Context device C S(s) Pervasive Computing

64 Single Sensor vs. Multiple Sensors
Single but powerful sensor: Position sensor, video sensor (camera) Useful information is inferred from what is perceived Prior knowledge is needed for information useful Simple but cheap sensors: Each sensor captures one facet of the context 例如:我要先知道辦公室的佈置 (例如電話在哪裡),位置的資訊對我才有用 Pervasive Computing

65 Take Multiple-Sensor Approach
Need to combine multiple simple sensors vs. one camera with powerful recognition capability Gain rich data to infer useful context with little computation Real world situations: situations and sensors 用一個camera,照樣可以捕捉一切情形。可是:怎麼處理? Pervasive Computing

66 Example: TEA Technology Enabling Awareness
Motivation: make personal mobile devices smarter Specs: CPU: PIC16F877 Storage: 8K EEPROM RAM: 200 Byte Use serial line to communicate with the host host TEA Pervasive Computing

67 TEA Architecture Cue: abstraction of raw data
For example, an acceleration sensor can infer cues like pattern of movement and current speed Rules from cue to context: can be pre-defined or use supervised/unsupervised learning 請學高中物理 不能太複雜,必須用簡單的計算就可達到 另外的例子前面提過的,從location可以知道contain的關係, 從tilt可以知道是正拿反拿 (sensor可能會說傾斜幾度,但cue只要告訴正反即可) 問題:既然這是一個generic的plugin,那麼可能會面對多種context,怎麼可能預先設想? Pervasive Computing

68 Initial Exploration Tried on both PDA and mobile phone, and found it more useful on mobile telephony Two analysis strategies: Analyze how well a sensor contributes to a given context Analyze which sensors relate to which context Finding: audio, motion and light are useful globally, and other sensors are only useful in specific contexts Pervasive Computing

69 Implementation Some data are directly analyzed and not stored (audio)
Some data are stored and processed (acceleration, light) Some data are cues itself; no need to process (temperature, skin conductance) Rules from cue to context are extracted offline and hardwired 例如:對加速度作一次微分 Pervasive Computing

70 Application Profile activation:
A user can have many profiles Activate profiles according to the situation (in-hand, on-table, in-pocket, outdoors) 87% certainty, 30 sec to calculate (not yet optimized) Context sharing (and privacy invasion): Ask to call B Request B’s context A Provide context Inform A Make decision (Voice or short message) A’s phone B’s phone Pervasive Computing

71 Mediacup: An Active Artifact
Spec: 1Mhz CPU, 15K storage, 384 byte RAM Digital temperature sensor 3-ball switch Detect movement A switch Detect whether placed on surface Infrared diode for comm. Capacitors charged wirelessly 3mm high Pervasive Computing

72 Identified Contexts Movement context: Temperature context:
Cup is stationary, drinking out of the cup, cup is played with, cup is carried around Computed by rule based heuristics with a short history of movements Temperature context: Filled up, cooled off, current temperature Updated every 2 seconds The cups broadcast their context to an overhead transceiver using the infrared diode Pervasive Computing

73 Design Issues Power management Transparency System kept very low-end
Motion detection uses interrupt instead of polling to enable sleep mode in 99% of the time Put the cup on the saucer to recharge wirelessly (15 minute for 12 hour) Transparency Hiding the technology does not suffice In the battery-charged prototype, users forget to replace the battery because the effect of technology is invisible! Pervasive Computing

74 Design Issues (Cont’d)
Region of impact No context-aware application in the environment Context as a common resource Inside available MEETING Outside Pervasive Computing

75 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Handling Multiple Contexts Context Modeling Context Programming Summary Pervasive Computing

76 Context Modeling Source:
“Modeling Context Information in Pervasive Computing Systems,” Karen Henricksen, Jadwiga Indulska, Andry Rakotonirainy School of Information Technology and Electrical Engineering, The University of Queensland Pervasive Computing

77 Characteristics of Context Information
Exhibits a range of temporal characteristics Static vs. dynamic (highly variable in persistence) Static (birth day) may be obtained from user, but dynamic often from indirect means, e.g. sensors Context histories (past and future) May be incorrect, inconsistent, incomplete Failure, faulty info, transmission delay, ... Has many alternative representations at different level of abstraction, relations between representations Is highly interrelated (by derivation rules) Pervasive Computing

78 Core Modeling Concepts
Object-based approach: Entities: a physical or conceptual object Attributes: properties of entities Associations: linking an entity, its attributes, and other entities (uni-directional) Owner of an association As assertions about the owning entity Context description is a set of such assertions Pervasive Computing

79 An Example Scenario 3 entities: people, communication devices, communication channels Pervasive Computing

80 Classifying Associations
Static associations: relationship fixed during lifetime of owner Dynamic association: Sensed: transformed from raw data, change frequently, staleness, sensing errors Derived: derivation function from other associations, e.g., is located near Profiled: supplied by users Pervasive Computing

81 Structured Constraints on Associations
Simple association: owner appears only once in this role, e.g., named association of Person Composite associations: Collection: owner can be associated with multiple attributes and entities, e.g., people work with others Alternative: e.g., comm. channel require a device Temporal: association for a time interval, e.g., user activities Pervasive Computing

82 Annotated Example Pervasive Computing

83 Modeling Dependencies
A dependency is a special type of relationship that exists between associations Capture the reliance of one association on another An association, a1, dependsOn, a2, iff a change to a2 has the potential to cause a change in a1 e.g., battery life dependsOn network bandwidth A dependency can be qualified by a participation constraint, e.g., engaged in and located at are dependent if they describe the same person Pervasive Computing

84 Dependencies in the Example
Pervasive Computing

85 Modeling Context Quality
Quality support: annotate associations with a number of quality parameters Each parameter is described by one or more quality metrics, which represent precise ways of measuring context quality with respect to the parameter Application dependent: e.g., user location: accuracy, freshness e.g., user activity: certainty, accuracy (both prob. values) Pervasive Computing

86 Example with Context Quality
Pervasive Computing

87 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Handling Multiple Contexts Context Modeling Context Programming Summary Pervasive Computing

88 Context Toolkit “The Context Toolkit: Aiding the Development of Context-Enabled Applications” D. Salber, A.K. Dey, G.D. Abowd Georgia Institute of Technology Proc. Of CHI’99 Pervasive Computing

89 Difficulties in Using Context
Is acquired from unconventional sensors Must be abstracted to make sense for the applications May be acquired from multiple distributed and heterogeneous sources Is dynamic Need to detect changes in real time and adapt to constant changes, history information is valuable => Lack conceptual model and tools to solve => Learning from GUI => context widgets Pervasive Computing

90 GUI Toolkits Benefits Limitation
hide specifics of physical interaction devices manage the detail of the interaction provide reusable building blocks Limitation lacking flexibility Pervasive Computing

91 Context Toolkit Toolkit for distributed context-aware apps
Framework for acquiring and handling context Standard components Three key abstractions Widgets, Interpreters, and Aggregators Pervasive Computing

92 Context Widgets Widgets abstract out sensors App App Location Widget
Active Badge Active Badge GPS Cell Phone Location Pervasive Computing

93 Context Widget A software component that Analogy to GUI widget
provides applications with access to context information from their operating environment insulates applications from context acquisition Analogy to GUI widget Separation, callbacks, attributes, encapsulation, abstraction e.g. GUI button Why: Responsible for acquiring and abstracting data from particular sensor, separation of concerns, storage Pervasive Computing

94 Features Context widgets have a state and a behavior
State: a set of attributes that can be queries or subscribed (via widget triggers) by applications e.g., IdentityPresence (location and identity of a user) Are basic blocks managing sensing of context need means for composing widgets 3 components: generator, interpreter, server Differences from GIU widgets: Distributed, acquired from multiple sources Alive always, not activated by applications (e.g., environmental information monitoring) Pervasive Computing

95 Context Interpreters Convert or interpret context to higher level information App Location to Room Interpreter Location to Street Interpreter Location Widget Pervasive Computing

96 Context Aggregators Collect contexts relevant to particular entities
Further separation, simplifies design App Person Aggregator Activity Widget Affect Location to Room Interpreter Pervasive Computing

97 Context Services Perform behaviors that act on the environment In/out
board Pervasive Computing

98 Context Discoverer Registry for context components In/out board
Pervasive Computing

99 Example Context Widgets (I)
IdentityPresence: senses the presence of people and their identity Generators Acquires raw information from the sensor(s) Could be voice recognition, Active Badges, video/image recognition, keyboard and login information Attributes and callbacks are independent of generators Pervasive Computing

100 Example Context Widgets (II)
Activity: senses the current activity level at a location Generators: Microphone, infrared sensor, video image analysis Pervasive Computing

101 Other Context Widgets NamePresence: PhoneUse: MachineUse:
Provides the user’s actual name PhoneUse: Whether a phone is being used and the length of use MachineUse: When a user logs onto or off of a computer, his identity, and length of her computing session GroupURLPresence: Provides a URL relevant to the research group a user belongs to when her presence is detected Pervasive Computing

102 Example of Using Context Widgets
In/out board: One IdentityPresence at entrance to building Pervasive Computing

103 Example of Using Context Widgets
Information Display: Displays information relevant to the user’s location and identity on a display adjacent to the user Activates itself as someone approaches it, and the information it displays changes to match the user, her research group, and location. Context : location of the display, the identity of the user, the research group the user belongs to and information that is interesting to that research group GroupURLPresence or IdentityPresence Pervasive Computing

104 Example of Using Context Widgets
DUMMBO (Dynamic Ubiquitous Mobile Meeting Board) Digitizing whiteboard to capture and access informal and spontaneous meetings Capture ink written to and erased from whiteboard as well as the recorded audio discussion Activated when two or more people gathered around Context: participants’ identities, time of arrival at or depart whiteboard, location of whiteboard Multiple NamePresence, one for each location where DUMMBO could be moved to, one on DUMMBO itself Pervasive Computing

105 Context Implementation Details
Handle composition: e.g., IdentityPresence + Activity  Meeting Interpreters can be used to assess validity of uncertain information provided by multiple generators Server: a widget that collects, stores and interprets information from other widgets Often used to model context of real world entities, e.g., users or places, from elementary widgets May include privacy handling Pervasive Computing

106 Context Implementation Details
Handle communicating across heterogeneous components: Assume that the underlying system supports TCP/IP Communication model uses the HTTP protocol Language model uses the ASCII-based Extensible Markup Language (XML) Pervasive Computing

107 Context Implementation Details
Handle dynamism: Environment changes  application adapt Need: to access information and to access only desired information, also need to access context history Use a subscription mechanism to notify an application of context changes and a polling mechanism to allow an application to inquire Allow conditions to be specified before the widgets will be notified  filtering unwanted info at infrastructure All widgets store historical data in a database for applications or interpreters to retrieve Pervasive Computing

108 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Handling Multiple Contexts Context Modeling Context Programming Summary Pervasive Computing

109 Context-Oriented Programming
Context Oriented Programming (COP) for Pervasive Computing, Andry Rakotonirainy, University of Queensland, Australia Pervasive Computing

110 Motivating Example A portable Hello World program Pervasive Computing

111 Basic Idea “open term” expression:
Printer: print_on_printer(v) Speaker: ascii2voice(v) Monitor: display(v) Open terms, also called “gaps” or “holes”, are the core programming construct of COP Open term filling Open terms allow us to create new terms and bind them dynamically within the scope of a program. Dynamic binding Dynamic scoping Pervasive Computing

112 Open Term Like macro expansions Pervasive Computing

113 Compared with Traditional Way
Pervasive Computing

114 Context Oriented Programming
Formalization A process P is adapted to a process Q when P enters a context m. The open term “output” in P is filled with an expression ascii2voice from the context m. Implementations Use Python as a language to express COP Use XML to describe context Pervasive Computing

115 Python
Python is an interpreted, interactive, object-oriented programming language. It is often compared to Tcl, Perl, Scheme or Java. It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. Pervasive Computing

116 XML Specification of an Open Term
<CURRENT> It describes the current context. E.g <task> reading </task> <REQUIRE> It describes the required context of such expression. E.g <security> SSL </security> <EXCLUDE> If the “filling expression” features this context, it shouldn’t fill the open terms. <OS> Palm OS </OS> means that an function from a Palm OS shouldn’t fill the current open term. Pervasive Computing

117 Dynamic Scoping COP allows a form of dynamic scoping. Variables in COP can be called (set/get) from outside the block of code in which they are defined. By default, all internal variable can be bound (read/write) to external variables. I think that this is not a good idea because the program will lose its readability! Pervasive Computing

118 COP Architecture (1/2) Objects that contain open terms
Objects providing contextualized terms Objects that performs the matching between gaps and offering functions Pervasive Computing

119 COP Architecture (2/2) Registry register function 1
lookup matching function 2 3 Context aware object with migratable code Context aware object with gaps migrate code 4 context awareness context awareness Pervasive Computing

120 COP Architecture Pervasive Computing

121 My Example (1/2) class Context { public: virtual int GetValue() = 0;
}; class Temperature : public Context int GetValue() { return 30; } Pervasive Computing

122 My Example (2/2) Object: using the context via common interface
Context ctx; //get context prototype (Temperature) //download & fill context body if (ctx.GetValue() > 25) printf(“Today is hot!\n”); else printf(“Today is cold!\n”); Pervasive Computing

123 Outline Motivation Context and Context-aware Computing
Context-aware Applications Developing Context-aware Applications Issues and Challenges Special Topics Summary Pervasive Computing

124 Summary Strides in sensors, recognition, and wireless are enabling new context applications Still need to lower barriers to entry Lots of systems issues, even more people issues Lots of potential here for new kinds of interaction and applications Pervasive Computing

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