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Context Aware Paradigm for Pervasive Computing Environment
 

Context Aware Paradigm for Pervasive Computing Environment

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Presentation of MS Thesis defense by Umar Mahmud on 18th September, 2006

Presentation of MS Thesis defense by Umar Mahmud on 18th September, 2006

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  • Highly dynamic nature of context and the presence of replicated services requires a prudent search mechanism for the appropriate service for the mobile user
  • Design of a Context-Aware System that provides smart service discovery in pervasive environments.
  • The low-level context is firstly categorized and then interpreted to give meaning to the context
  • The deduced who is the user ID and the user role associated with the ID.
  • Decision Making in CAPP answers the question of Which Service? In a typical scenario the decision making module is met with a single user context and multiple service contexts
  • In the computing era of pervasive computing, context-awareness being a prime issue demands a context-aware system Undoubtedly, context-awareness is the future of computing and will change the way services are accessed By the Grace of Almighty Allah, the objectives that were set forth in the start of this thesis have been successfully accomplished

Context Aware Paradigm for Pervasive Computing Environment Context Aware Paradigm for Pervasive Computing Environment Presentation Transcript

  •  
  • Context-Aware Paradigm for Pervasive Computing Environment (CAPP) Context-Aware Smart Service Discovery in Pervasive Environments
  • GEC Members
    • Dr. Farrukh Kamran, CASE Islamabad
    • Dr. Muhammad Akbar, CI(E Div), MCS
    • Dr. Amir Qayyum, CASE Islamabad
    • Lt. Col Naveed Sarfraz, HoD, CS Dept, MCS
  • Sequence of Presentation
    • Introduction
    • System Design
      • Context Congregator
      • Context Interpreter
      • Decision Making Module
    • Evaluation & Analysis
    • Epitome
  • Introduction
  • Pervasive Environment
    • Pervasive computing is a term for the strongly emerging trend towards
      • Numerous, casually accessible, often invisible computing devices
      • Frequently mobile or embedded in the environment
      • Connected to an increasingly ubiquitous network infrastructure composed of a wired core and wireless edges
    • Examples
      • Hospitals
      • Airports
      • Educational Institutes
  • Issues in Pervasive Computing
    • Context-Awareness
    • Trust, Security and Privacy
    • Seamless Communication
    • Low Powered Devices
    • Self Configuration
    • Information Overload
    • Social Issues
    • Business Models
  • Problem Statement
    • Smart service discovery & its subsequent delivery to the mobile users by interpreting the users’ as well as the services’ context
      • Interpretation of the contextual information
      • Resource arbitration
        • Delivery of the best service available, among a pool of similar services, to the user
  • Context-Awareness
    • First coined by Schilit in 1994
    • Refers to the devices that have information about the circumstances under which they operate and can react accordingly
    • “ Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.”
    • -- Anind Key Dev, GA Tech
  • Feature Comparison of Existing Systems      GAIA OS (2002)      CAMUS (2005)      SOCAM (2004)      CASS (2004)      CoBrA (2003)      Hydrogen (2002)      TEA      CAPEUS      CMF (2003)      Context Toolkit(1999)      Classroom & GUIDE      Xerox PARC(1992)      Active Badge (1992) Reasoning Support Resource Discovery OWL based Ontology Decentralized Architecture Rich Context
  • Limitations of Present Systems
    • Early context aware systems were only location based
      • Need of a comprehensive and rich context data representation
      • OWL based ontology
    • Existing context-aware systems are problem specific
      • Context-aware applications
      • Custom reasoning engines
  • Research Objectives
    • Design of a Context-Aware System
      • Organization of contextual information
      • Representation of the contextual information
      • Interpretation of the contextual information
      • Best service selection for the user
  • System Design
  • Architectural Foundation
    • Design Principles of Distributed Systems
      • Service-Oriented Architecture (SOA)
    • Context Acquisition
      • Context Server Approach
    • Context Management
      • Networked Services Approach
    • Context Representation
      • Ontology Based Models
        • OWL (Web Ontology Language)
  • Design of CAPP
  • Design of CAPP (contd.) Receives interpreted context and decides as which is the best available service that should be delivered to the client or returns a list of probable services to the client Description Makes decision on which service is to be delivered to the client on the basis of interpreted context and weighted averages of the interpreted contexts Function Decision Making Module This module interprets the gathered context by and identifying Who, What, Where & When contexts Description To interpret the gathered context as Who, What, When & Where contexts of both the user and the services Function Context Interpreter This module gathers context data. Description Gather context data on the basis of client’s info and request and represent the gathered information in the form enforced by the rule Repository Function Context Congregator This module listens to incoming requests and forwards them to the congregator for context gathering. Description Receives request and dispatches it to the context congregator module. Returns the result to the requester Function Dispatcher Module
  • Context Congregator
  • Context Space in CAPP
  • Representation Scheme in CAPP
    • OWL (Web Ontology Language)
      • OWL Full for rich context representation
    • Controlled Vocabulary
      • Set of keywords or phrases that are used to tag units of information for smart and efficient retrieval by a search
      • Services may have names or aliases
      • For example, a user asking for a temperature service could be replied by locating a weather service that is registered with the system
  • Vital Context
    • It is inefficient to gather all the data at all times or when a user has requested for an interaction
      • Some of the data is necessary for a particular interaction while the rest has no effect on the interaction
    • Gather only data that is critical to the interaction
      • A list of necessary data for each type of interaction has been identified and is maintained in the context congregator
  • Context Interpretation
  • Context Processing Context Congregation Context Interpretation Raw, Low-Level Context Interpreted, High-Level Context (Data from Sensor Services) (To The Decision Making Module) Context Processing
  • High-Level View of Context
  • Interpretation in CAPP
    • Categorization of contextual data
    • The high-level context identifies the ‘ Who’, ‘What’, ‘Where’ and ‘ When’ contexts of both the users’ as well as the services’
      • ‘ Who’ - identification and description parameters
      • ‘ What’ - user’s request and the services’ capabilities
      • ‘ Where’ - the location and environmental parameters
      • ‘ When’ - time of request and time of delivery
  • Interpretation in CAPP (contd.)
    • High-Level User Context
      • Deduced Who
      • Deduced When
      • Deduced Where
      • Deduced Health
    • High-Level Service Context
      • Deduced Who
      • Deduced What
      • Deduced When
      • Deduced Where
  • Deduced Who (User)
  • Deduced When (User)
  • Deduced Where (User)
  • Deduced Health (User)
  • Interpreted User Context
    • “ User Deduced User (User) has requested a Deduced What (User) service at Deduced When (User) time. The user wants the service to be delivered at Deduced Location (User) while he/she is involved in activity Deduced Activity (User) and has health condition Deduced Health (User)”
  • Deduced Who (Service)
  • Deduced What (Service)
  • Deduced When (Service)
  • Deduced Where (Service)
  • Interpreted Service Context
    • “ Service Deduced Service (Service) is of type Deduced What (Service) . The service is located at location Deduced Where (Service) and will be available at time Deduced When (Service)”
  • Decision Making Module
  • Which Service Decision Making Module User Context Service Context 2 Service Context 1 Service Context N Service Context J User Context Output Inputs Best Association WHICH
  • Which Service (contd.)
    • Deduced What (User) vs. Deduced What (Service)
      • Primary type of the service or the secondary type of the service
    • Deduced When (User) vs. Deduced When (Service)
      • Service that will be available the earliest to the service that will be delivered at the latest
    • Deduced Where (User) vs. Deduced Where (Service)
      • Services starting from the nearest to the farthest service
    • Deduced Health (User)
      • The user health imposes restrictions on the selection of the service
  • Which Service (contd.)
    • The final selection process selects the best service for the user
      • weighted average scheme of What, Where, When and Health Values for any scenario is calculated as
    Where p, q, r and s are the weights
  • Decision Process
  • Evaluation & Analysis
  • Evaluation Criteria
    • Java based simulation
      • Implementation of the flow models in interpretation and decision making
      • Test cases from everyday interaction in CS department
    • Effectiveness of the CAPP
      • Successful if it is able to return a single service or a short list of probable services to the user for a scenario
      • Failure if it is unable to reach a conclusion either by returning a single service or a short list of probable services and returns list of all services
      • For the case when the user requests a service that is not listed in the smart space, informing the user that the service is not listed is also termed as a success
      • Successes should be at least 75% of the total number of cases
  • Assumptions
    • Each service type has at most and at least three services and each user has at most and at least three devices (mobile or traditional)
    • The contextual data is always available to the system and no ambiguous data is provided by the sensor services
    • Values assigned to priority levels are
      • High = 0.9
      • Medium = 0.5
      • Low = 0.1
  • Test Case 1
    • Ayesha a student of BE is sitting in BELAB and requests a Print service
      • There are three print services in the department, one in MSLAB and two in BELAB
      • One of the services in BELAB is a Fax service that provides prints as a secondary capability
      • Ayesha’s health condition is Normal and has No health constraints
  • Test Case 1 - Vital Context PRINTMS PRINTBE02 PRFAXBE ID Normal Sugar level NULL NULL Print Secondary type 38.2 C User temp Print Print Fax Primary type 71 Pulse Admin Admin Admin Owner 110 Di Sistolic Fit Fit Fit Air quality 80 Sistolic High High High Noise Normal Noise Normal Normal Normal Humidity Normal Humidity 16 C 16 C 16 C Temperature 16 C Temperature Normal Normal Normal Dust level Normal Dust level Bright Bright Bright Illumination Bright Illumination 18 minutes 10 minutes 25 minutes Availability time Busy Activity of user 100 Mbps 100 Mbps 100 Mbps Bandwidth Active, Idle, Idle Status Busy Busy Busy Status PDA, smart phone, PC Type 44 67 64 Space avail 3 Devices 50 60 65 Queue length Student Role Service C Service B Service A Attribute Ayesha Attribute Service Contexts User Context
  • Test Case 1 - Vital Context (contd.) Service Contexts User Context Service C Service B Service A Attribute Ayesha Attribute 1130 hrs 1130 hrs 1130 hrs Time 1130 hrs Time 18 June, 2006 18 June, 2006 18 June, 2006 Date 18 June, 2006 Date 0 0 0 Speed BELAB Location NULL NULL NULL Direction 0 Speed False False False Mobility NULL Direction MSLAB BELAB BELAB Location False Mobility 8080 8080 8080 Port Sitting Orientation //mslab017//printms //belab075//printbe02 //belab023//prfaxbe Namespace NULL Alias 192.168.13.201 192.168.13.181 192.168.13.33 IP Ayesha Ahmad Name NULL NULL BEPRINTANDFAX Alias 37405-0521200-6 SSN PRINTCS PRINTBE PRINTFAX Name AYESHA_BE_08_37 ID
  • Test Case 1- Results 0.5 0.5 0.1 0.9 Service C 0.9 0.9 0.9 0.9 Service B 0.63 0.9 0.9 0.1 Service A Weighted Average When Value Where Value What Value
  • Test Case 1 - Output
  • Test Case 2
    • Kashif a student of BE is sitting in MSLAB and requests a Scan service
      • There are three print services in the department, two are in MSLAB and one is in BELAB
      • Kashif’s health condition is Not Normal and has Fever as his health constraint
  • Test Case 2 – Vital Context SCANMS2 SCANMS SCANBE ID Normal Sugar level NULL NULL NULL Secondary type 102 F User temp Scan Scan Scan Primary type 71 Pulse Admin Admin Admin Owner 120 Di Sistolic Fit Fit Fit Air quality 82 Sistolic Normal Normal Normal Noise Normal Noise Normal Normal Normal Humidity Normal Humidity 16 C 16 C 25 C Temperature 25 C Temperature Normal Normal Normal Dust level Normal Dust level Normal Bright Bright Illumination Bright Illumination 0 minutes 0 minutes 0 minutes Availability time Busy Activity of user A.C. A.C. A.C. Power supply Active, Active, Active Status Idle Idle Idle Status PC, PDA, Laptop Type 0 0 0 Space avail 3 Devices 0 0 0 Queue length Student Role Service C Service B Service A Attribute Kashif Attribute Service Contexts User Context
  • Test Case 2 – Vital Context (contd.) Service Contexts User Context Service C Service B Service A Attribute Kashif Attribute 1215 hrs 1215hrs 1215hrs Time 1215 hrs Time 28 July, 2006 28 July, 2006 28 July, 2006 Date 28 July, 2006 Date MSLAB MSLAB BELAB Location MSLAB Location //mslab065//scanms2 //mslab096//scanms //belab024//scanbe Namespace Standing Orientation 192.168.13.129 192.168.13.125 192.168.13.26 IP Kashif Farooq Name NULL NULL NULL Alias 37405-126524-9 SSN MSSCAN2 MSSCAN BESCAN Name KASH_BE_05_199 ID
  • Test Case 2 - Results 0.375 0.1 0.9 0.9 0.9 Service C 0.375 0.1 0.9 0.9 0.9 Service B 0.575 0.9 0.9 0.1 0.9 Service A Weighted Average Health Value When Value Where Value What Value
  • Test Case 2 - Output
  • Test Case 3
    • Waqas a faculty member of CS Department is sitting in faculty offices and requests for a Multimedia Projector service
      • There are three projector services in the department, one in LECTUREHALL01, one in LECTUREHALL02 and one in MSLAB
      • Waqas’s health condition is Normal and has No health constraints
  • Test Case 3 - Vital Context 1030 hrs 1030 hrs 1030 hrs Time 1030 hrs Time July 28, 2006 July 28, 2006 July 28, 2006 Date July 28, 2006 Date MSLAB LECHALL02 LECHALL01 Location Faculty office Location NULL NULL NULL IP Sitting Orientation NULL NULL NULL Alias Viky Alias MULTIMEDIAMSLAB MULTIMEDIALECHALL2 MULTIMEDIALECHALL1 Name Waqas Arshad Name MMSLAB ML2 ML1 ID 37405-053320-8 SSN NULL NULL NULL Secondary type WAQ_FAC_CS_23 ID Multimedia Projector Multimedia Projector Multimedia Projector Primary type Busy Activity of user 0 minutes 0 minutes 50 minutes Availability time Idle, Active, Active Status Idle Idle Busy Status PC, phone, laptop Type 1 1 1 Space avail 3 Devices 0 0 1 Queue length Faculty Role Service C Service B Service A Attribute Waqas Attribute Service Contexts User Context
  • Test Case 3 - Results 0.633333333 0.9 0.1 0.9 Service C 0.633333333 0.9 0.1 0.9 Service B 0.366666667 0.1 0.1 0.9 Service A Weighted Average When Value Where Value What Value
  • Test Case 3 - Output
  • Test Case 4
    • Maryam a faculty member is standing in the Hallway of CS department and requests a Telephone service as she wants to make a phone call
      • Maryam being new to the CS department requires the system to select a telephone service for her
      • There are three telephone services in the department, one is in MSLAB, one is in BELAB and one is in the faculty office
      • Maryam’s health condition is Normal and has No health constraint
  • Test Case 4 - Vital Context 1227 hrs 1227 hrs 1227 hrs Time 1227 hrs Time 29 July, 2006 29 July, 2006 29 July, 2006 Date 29 July, 2006 Date FACULTYOFFICE BELAB MSLAB Location Standing Orientation FACULTYTELEPHONE BELABTELEPHONE MSLABTELEPHONE Name Maryam Ayaz Name TELFAC TELBE TELMS ID 37405-056204-7 SSN NULL NULL NULL Secondary type MARY_FAC_EE_057 ID Telephone Telephone Telephone Primary type Idle Activity of user Admin Admin Admin Owner Idle, Idle, Idle Status 0 minutes 0 minutes 0 minutes Availability time PDA, PDA, Laptop Type A.C. A.C. A.C. Power supply 3 Devices Idle Idle Idle Status Faculty Role Service C Service B Service A Attribute Maryam Attribute Service Contexts User Context
  • Test Case 4 - Results 0.633333333 0.9 0.1 0.9 Service C 0.633333333 0.9 0.1 0.9 Service B 0.633333333 0.9 0.1 0.9 Service A Weighted Average When Value Where Value What Value
  • Test Case 4 - Output
  • Performance
    • Success rate
      • In the scenarios there were three successes and one failure
      • Only cases where there are no health constraints and the weights are set to 1, are considered
    • The total number of values where there are three services and each service can have the same three values where ‘A’ is the all possible combination when ‘V’ is the value levels for ‘N’ number of services is given as
  • Performance (contd.) A B C 0.5 0.5 0.5 14 A B 0.1 0.5 0.5 15 C 0.9 0.1 0.5 16 A C 0.5 0.1 0.5 17 A 0.1 0.1 0.5 18 B C 0.9 0.9 0.1 19 B 0.5 0.9 0.1 20 B 0.1 0.9 0.1 21 C 0.9 0.5 0.1 22 B C 0.5 0.5 0.1 23 B 0.1 0.5 0.1 24 C 0.9 0.1 0.1 25 C 0.5 0.1 0.1 26 A B C 0.1 0.1 0.1 27 C 0.9 0.5 0.5 13 B 0.1 0.9 0.5 12 B 0.5 0.9 0.5 11 B C 0.9 0.9 0.5 10 A 0.1 0.1 0.9 9 A 0.5 0.1 0.9 8 A C 0.9 0.1 0.9 7 A 0.1 0.5 0.9 6 A 0.5 0.5 0.9 5 A C 0.9 0.5 0.9 4 A B 0.1 0.9 0.9 3 A B 0.5 0.9 0.9 2 A B C 0.9 0.9 0.9 1 Outcome Service C Service B Service A
  • Performance (contd.)
    • There are 3 failures out of a total 27 possible combinations
      • 15 cases resulted in selection of single service while 9 cases provided a short list of the probable services
    • The success rate, where ‘F’ is the failure rate when ‘f’ is the number of failures among ‘A’ possible combinations and ‘S’ is the success rate is given as
  • Epitome
  • Limitations of CAPP
    • Availability of the context sensing devices or the sensor services
    • The data generated by the devices is very large
      • History provides predicted contextual data to the system
    • Security
      • The confidentiality can be provided by encrypting and then storing the data
      • Gathering data only from the trusted sensor services present in the environment
  • Advantages
    • Modularity
    • Scalability
    • Service orientation
    • Incorporating multiple sensor sources
    • Industry standards
    • Support to asynchronous as well as synchronous calls
    • Atomicity
    • Heterogeneity
    • Transparency
  • Conclusion
    • Context-Aware Systems
      • Context Acquisition
      • Context Representation
      • Context Interpretation
      • Decision Making
    • CAPP is a service oriented framework that provides context-aware smart service discovery and delivery
      • Robust, scalable & modular
      • Loose coupling
      • conforms to the object-oriented features of encapsulation and information hiding
  • Latest References
    • T.P. Moran and P. Dourish, “Context-Aware Computing,” Special Issue of Human-Computer Interaction, vol. 16, 2001
    • A.K. Dey, “Understanding and Using Context”, Personal and Ubiquitous Computing , Vol. 5, Issue 1, pp. 4 – 7, 2001.
    • M. Baldauf, S. Dustdar, and F. Rosenberg, “A Survey on Context-Aware Systems,” International Journal of Ad Hoc and Ubiquitous Computing, 2004.
    • T. Strang, and C. Linnhoff-Popien, “A Context Modeling Survey,” First International Workshop on Advanced Context Modeling, Reasoning & Management, 2004.
    • T. Strang, and C. Linnhoff-Popien, “A Context Modeling Survey,” First International Workshop on Advanced Context Modeling, Reasoning & Management, 2004.
    • M. Riaz, S.L. Kiani, S. Lee, S. Han and Y. Lee, “Service Delivery in Context Aware Environments: Lookup and Access Control Issues,” in proceedings of RTSCA’05, 2005.
  • Thank you