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					Collaborative Data Gathering Using
  Context-aware Mobile Devices
            Tejas Lagvankar
         Master’s Thesis Defense
           Advisor : Tim Finin
             20th July 2010


                                     1
                                       Agenda
•   Motivation
•   Related Work
•   Context
•   Framework Details
•   Results
•   Future Work
•   Conclusion

                                                                                                   2
    Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
        Motivation (Previous Work)
• SPIRE gathers data from citizen scientists who
  report observations about biological species.

• Spotter is the web-based tool to report
  observations.

• We developed SAM (Spotter App on Mobile)
  so that observations can be reported on the
  fly.
                                                                                                3
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
     Semantic Data                                                       Ease of Use
       Generation                                                      and Correctness




                                  How can we improve
                                     Field survey
                                    Mechanisms?




                                     - Get more volunteers
                                    -Dynamic Requirements




                                                                                               4
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                       SAM




                                                                                               5
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                                                                               6
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                                                                               7
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
     Semantic Data                                                       Ease of Use
       Generation                                                      and Correctness




                                  How can we improve
                                     Field survey
                                    Mechanisms?




                                     - Get more volunteers
                                    -Dynamic Requirements




                                                                                               8
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
     Semantic Data                                                       Ease of Use
       Generation                                                      and Correctness




                                  How can we improve
                                     Field survey
                                    Mechanisms?




                                     - Get more volunteers
                                    -Dynamic Requirements




                                                                                               9
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
            Getting more volunteers
• More volunteers
  – More data
  – Diverse data
  – More opinions
  – More confidence
• Why getting volunteers is a problem?
  – Volunteers are required to visit the site
    ‘intentionally’
  – Survey is about a specific region

                                                                                                10
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
  Handling dynamic requirements

• Information to be gathered as well as area from which
  information is needed may keep on changing.

• Make use of millions of mobile devices, that have
  spatial coverage.

• How to select appropriate users ? i.e. Users who are at
  the right place, right time and in a right state.


                                                                                                 11
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                           The Question
                                                              What is the
                                                           condition of the
            Internet                                          bridge near
                                                             Hilltop after
                                                            today’s snow ?




         Where
          ?
                                   NOW !


                   What
                      ?

                                                                                               12
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                        More Questions
• What is the current parking situation at the
  Commons parking lot ?

• Urgent ! Is there a surgeon nearby ?

• Can anyone spot the Red balloons put up by
  DARPA ?


                                                                                                13
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Properties of Queries



      Very Specific                                          Time Bound
    Answers may not be
                                                          Need the response in a
   already available on the
                                                         short period of time
   internet
                                                          Have to reach users on
    Need to ask actual
                                                         their mobile devices
   people




                                                                                               14
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
           Use of Social Networking
• Ask queries on Facebook, Twitter etc… where
  many people are online !
• However:
  – Unnecessary Flooding of queries to uninterested
    people
  – Still, possibility of people being in the right
    situation is very less



                                                                                                15
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      What do we need
• We need a system that
  – Is mobile based and can reach people at their
    presence.
  – Is able to select appropriate people who can
    answer any such queries




                                                                                                16
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
          Thesis statement

Develop a mobile based framework to
discover user context and facilitate forwarding
of specific data requests to appropriate
people based on this derived context and
personal preferences.




                                              17
                             Related Work
• Roy Want and Veronica Falcao and Jon Gibbons: The Active
  Badge Location System. ACM -Transactions on Information
  Systems. 10., 91–102 (1992)

• Gregory D. Abowd and Christopher G. Atkeson and Jason
  Hong and Sue Long and Rob Kooper and Mike Pinkerton
  and Usability Centre: Cyberguide: A Mobile Context-Aware
  Tour Guide. (1997)

• Keith Cheverst and Nigel Davies and Keith Mitchell and
  Adrian Friday and Christos Efstratiou: Developing a Context-
  aware Electronic Tourist Guide: Some Issues and
  Experiences. 17–24, (2000)

                                                                                                 18
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                            Related Work
• Location and mobility in a Sensor Network of
  Mobile Phones [Kansal and Zhao, 2007]




                                                                                                19
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                            Related Work
• A three tier system consisting of physical
  sensors, virtual sensors and the application
  tier.
• Mobile devices act as physical sensors over
  which a layer of virtual sensors is overlaid.
• Virtual sensors are static which may be points
  of interest for the applications.
• Any devices that come under these virtual
  sensors are contacted.
                                                                                                20
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                             Related Work
• Micro - blog [Gaonkar, S.; Li, J.; Choudhury, R. R.; Cox, L.; and
  Schmidt, A. 2008]




                                                                                                 21
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                            Related Work
• People can upload pictures and videos of the
  locations around – called as Micro-blogs.
• When a query for a particular region arrives, it
  is looked for in the micro-blog database.
• If not found, it is forwarded to the phones in
  the location of interest.



                                                                                                22
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                            Related Work
• The participatory applications discussed
  consider location alone as the deciding factor.
• Dealing with mobile phones, the context the
  user is in determines user participation and
  willingness.
• Our framework reasons about the user
  context before notifying the user.


                                                                                                23
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                    Context
• Context is a set of environmental parameters
  relevant to an application.
• We define it in terms of mapping the users’
  position to place.
• Position is the geo-spatial information
  captured in terms of lat-lon.
• Place is the conceptual information associated
  with the position depending on user
  surroundings and activities.
                                                                                                24
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                    Context (examples)
• Position : 39.255984,-76.711714
• Direct Mapping : UMBC
• Place : In a meeting, at the coffee shop,
  delivering a talk, in a class

• One position can map to multiple places.
• One place can map to multiple positions.

                                                                                                 25
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Framework Goals

• Select appropriate users depending on their
  context

• Derive conceptual place information about
  the user



                                                                                                26
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
 Framework : High Level Components
• Device
  – Mobile Agent
• Server
  – Data Collector
  – Request Collector
  – Notification Engine
  – Sensor / Context Database
  – Reasoning Engine

                                                                                                27
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
             Users of the Framework
• Applications using the framework will have 2
  primary roles:
  – Mobile User : End user of the system registered to
    the framework
  – Administrator : Manager Users, Manage System
    Rules, decide on data format and ways to collect
    data.



                                                                                                28
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      The Mobile Agent
• A piece of software that sits on the mobile device.
• Senses the environment using the device sensors.
• Sends three types messages:
   – INIT : Initial set up sent on first run
   – DATA : sends sensor data
   – ALIVE : check for notification
• Developed an agent using Google Android platform.
• Any other mobile agent following this pattern can talk
  to the server.


                                                                                                 29
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                    The Mobile Agent




                                                                                               30
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
             Contacts
                                                                        Light
               And
                                                                     Temperature
             Calendar




     Emails                                                                      GPS,
   Social N/W                                                                  Network
  Information                                                                  Location




                                                                        Motion,
                                                                        Magnetic
             Internet
                                                                         Field
                                                                        Sensors



                                                                                               31
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                       Sensors
•   Light                                           •   Battery
•   Temperature                                     •   User Present
•   Proximity                                       •   Motion
•   WiFi Status                                     •   Headset plugged
•   WiFi IDs                                        •   Power chord Status
•   GPS Coordinates                                 •   Bluetooth



                                                                                                   32
    Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                               The Server
• Divided into several components:
  – The Sensor data collector
  – The Request collector
  – The Reasoning engine
  – The Rule subsystem
  – The Context Database
  – The Notification Engine



                                                                                                33
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                                                                                               34
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
Data Collector and Notification Engine
• Data Collector
  – Collects data using HTTP, dumps into database
  – Can accept data from any mobile agents following
    the data format
• Notification Engine
  – Queues notifications for devices
  – Dispatches on ALIVE message



                                                                                                35
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Request Collector
• Accepts requests as HTTP requests and puts
  them into the queue.
• Parameters required:
  – GPS location
  – Request String




                                                                                                36
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                Submitting a request




                                                                                               37
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                 The Reasoning Engine
• Pre-reasoning:
   – Data extracted as RDF triples in N3 format at pre-defined intervals.
   – E.g.

   cs:29 cs:hasTimestamp "2010-06-13 01:54:43.0";
         cs:sensedLight "225";
         cs:hasBattery   "60".

• Reasoning:
   – Forward chaining reasoner works upon the triples.
   – Applies rules to these triples.
   – New triples are generated which constitute the context information
     for the user.



                                                                                                 38
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                The Reasoning Engine
• The framework provides CWM reasoner.
• However, any reasoner which can deal with
  N3 RDF can be used by the application.




                                                                                                39
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
               System and User Rules
• System Rules:
  – provide a level of abstraction over the raw sensor data
    and maps it to human readable terms.
  – are generic for all the users.
  – E.g.
        • if sensed light < 225, phone is in DARK environment.
        • If timestamp < 12, its MORNING etc..
  – The framework provides basic system rules for certain
    sensors like ambient light, timestamps and proximity.
  – Administrators can override and add to the system
    rules.
  – Provides ways of enforcing policies / group level rules.
                                                                                                40
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
               System and User Rules
• User Rules
  – allow mobile users to define conditions that
    constitute specific context information
  – E.g. the user may define conditions under which
    he can be considered as busy.
  – The user can set reminders depending on location,
    time, people around etc…



                                                                                                41
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
               The Reasoning Process
• Dr. Mark is advisor of Bob.
• Dr. Mark has the following triples:
        :Mark
        geo:lat "39.2546";
        geo:lon "-76.7120";
        cs:hasBluetooth 00:0A:D9:EB:66:C7.

• Bob has the following triples:
        :Bob
        geo:lat "39.2321";
        geo:lon "-76.7012";
        cs:hasBluetooth 00:0C:F3:2G:23:56;
        cs:hasNeighbour 00:0A:D9:EB:66:C7.


                                                                                                42
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                The Reasoning Process
• Bob has the following rules:
{?A cs:hasNeighbour ?B. ?C cs:hasBluetooth ?B.}
                                         => {?A cs:isNearBy ?C}.
{?A cs:hasBluetooth "00:0A:D9:EB:66:C7”}
                              => {?A cs:hasIdentity "Dr. Mark"}.
{?A cs:isNearby ?B. ?B cs:hasIdentity "Dr. Mark".}
                                         => {?A cs:isBusy "True"}.

• Bob has the following triples:
         :Bob
         geo:lat "39.2546";
         geo:lon "-76.7120";
         cs:hasLocation "ABC University";
         cs:hasBluetooth 00:0C:F3:2G:23:56;
         cs:hasNeighbour 00:0A:D9:EB:66:C7;
         cs:isNearby "Dr. Mark";
         cs:isBusy "True".


                                                                                                 43
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
            Guidelines for application
• Implement mobile agent, provide sensor
  information to the server.
• Choose the desired reasoning engine ( we
  provide results for CWM).
• Implement an interface for the request collector.
• Set up data repository and data reporting
  methods.
• Define system rules and provide default user
  rules.
• User Management (Login, UIs for rules etc…)

                                                                                                 44
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
    Guidelines (example for SPIRE)
• Android mobile agent
• SAM used as reporting tool, SPIRE DB for data
  collection.
• Rules defined for finding is user is busy or not.
• Request Collector and Notification Engine
  used as default.



                                                                                                45
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                     Context Discovery
• Broader goals is to find conceptual place
  information such as “in a cafeteria”, or “at a
  meeting”.
• Currently, we ask users to tag certain sensed
  information.
• System maintains list of seen WiFi IDs.
• Asks user to tag these with conceptual
  information.

                                                                                                46
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                    Context Discovery




                                                                                               47
Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Context Discovery
• Eventually, the system can have multiple tags for
  same WiFi ID.
• The most popular once can be used as
  suggestions.
• Will simplify understanding places and writing
  rules.
   – E.g. If a WiFi ID say “Simon” is tagged as “Food Court”
     and some time interval is tagged as “Lunch Time” the
     user could say:
{?D cs:hasWiFi ?W. ?W cs:hasTag "Food Court".
?D cs:hasTime ?T. ?T cs:hasConcept "Lunch Time".}
                    => {?D cs:notifyFoodLocation "True"}.


                                                                                                 48
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                     Context Discovery
• Similarly, seen bluetooth IDs can be tagged to
  form groups of people identifying a
  conceptual place.
• E.g. People in the class, people at the gym,
  people in the train.




                                                                                                49
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                     Evaluation Criteria
• The framework was built for developing
  applications for the SPIRE project and will be
  using SAM as the data reporting tool.
• Evaluation majorly includes a feature
  comparison, battery usage and reasoning
  times.



                                                                                                 50
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Evaluation Criteria
• Feature Comparison
Feature \ Apps                  FaceBook          MobSense         Micro-Blog      Ebiquity CAS

Social Querying                 Yes               Yes              Yes             Yes
Real time                       No                No               Yes             Yes

Context Aware                   No                No               No              Yes

People selection /              Broadcast         Broadcast        Select          Select
Broadcast
System/User level policies      No                No               No              Yes




                                                                                                  51
   Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Evaluation Criteria
• Battery Life
                                 Samples           % Usage for
                                                   5 hours with
                                                   Agent
                                 Sample 1          6%

                                 Sample 2          7%

                                 Sample 3          7%



* Samples taken for a Google Nexus one with all sensors on and for a regular phone usage



                                                                                                  52
   Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                      Evaluation Criteria
• Reasoning times with CWM
# Triples               Time Sample 1             Time Sample 2            Time Sample3

100                     2.719 s                   2.523 s                  2.707 s

200                     10.162 s                  10.061 s                 9.903 s

500                     10.876 s                  10.768 s                 10.982 s

1000                    6 m 7.904 s               6 m 7.710 s              6 m 7.543 s




                                                                                                  53
   Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                              Future Work
• Supervised learning for context discovery.
   – Train classifiers based on sensor values and apply
     label present in calendar.
   – E.g. For a certain calendar activity, record sensor
     values.
   – The classifier is then downloaded on the phone.
   – Now, using sensor values, can determine un-
     annotated calendar events.


                                                                                                 54
  Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                             Future Work
• Inter-device communication
  – Currently, all devices communicate with the server
    alone.
  – Context discovery can be faster if place
    information is shared with neighboring devices as
    well as stationary network elements like WiFi
    routers, Cell towers etc…




                                                                                                55
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                             Future Work
• Use of Google message push for more
  accurately timed notifications.
  – Reasoning takes time.
  – Currently, mobile agent communicates with server
    two times:
        • To report sensor data
        • To check for notifications
  – Using message push, we can eliminate the later.


                                                                                                56
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                               Conclusion
• We have developed a framework that takes a
  context-driven approach for social mobile
  applications requiring collaborative data
  gathering.
• We provide and suggest techniques for
  mapping position information to place
  information.


                                                                                                57
 Motivation  Related Work  Context  Framework Details  Results  Future Work  Conclusion
                              References
•   Kansal, A., Zhao, F.: Location and Mobility in a Sensor Network of Mobile Phones.
    ACM SIGMM 17th International workshop on Network and Operating Systems
    Support for Digital Audio and Video (NOSSDAV), (2007)

•   UMBC Ebiquity: http://spire.umbc.edu. UMBC Ebiquity, (2007)

•   UMBC Ebiquity: http://spire.umbc.edu/spotter. UMBC Ebiquity, (2007)

•   W3C: CWM (http://www.w3.org/2000/10/swap/doc/cwm.html, (2007)

•   Google: Aardvark (http://www.vark.com). (2010)

•   Cory Cornelius and Apu Kapadia and David Kotz and Dan Peebles and Minho Shin
    and Nikos Triandopoulos: AnonySense: Privacy-Aware People-Centric Sensing.
    Proceedings of the 2008 International Conference on Mobile Systems,
    Applications, and Services (MobiSys), 211–224 (2008)



                                                                                        58
                             References
•   Gaonkar, Shravan and Li, Jack and Choudhury, Romit Roy and Cox, Landon and
    Schmidt, Al: Micro-Blog: sharing and querying content through mobile phones and
    social participation. MobiSys ’08: Proceeding of the 6th international conference
    on Mobile systems, applications, and services, 174–186 (2008)

•   Roy Want and Veronica Falcao and Jon Gibbons: The Active Badge Location
    System. ACM Transactions on Information Systems. 10., 91–102 (1992)

•   Guanling Chen and David Kotz: A Survey of Context-Aware Mobile Computing
    Research.(2000)

•   Gregory D. Abowd and Christopher G. Atkeson and Jason Hong and Sue Long and
    Rob
•   Kooper and Mike Pinkerton and Usability Centre: Cyberguide: A Mobile Context-
    Aware Tour Guide. (1997)

•   W3C: RDF Primer (http://www.w3.org/TR/rdf-primer/), (2004)


                                                                                    59
      Thank You
         Dr. Finin
        Joel Sachs
         Dr. Joshi
         Dr. Yesha
         Dr. Laura
My Lab-mates and roommates
         Malavika

                             60
Discussion




             61

				
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