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CROWDSOURCING WITH
SMARTPHONES
Guide: Presented by:
Dr. Sheena Mathews Sabitha Subair
CS S7 B
Roll no: 101
CONTENTS
Introduction
What is crowd sourcing?
Benefits of crowd sourcing
Pitfalls of crowd sourcing
Applications of crowd sourcing
Crowdsourcing with Smart phones
Issues and characteristics
Applications of crowd sourcing with Smart phones
Smart trace
Crowd cast
Smartp2p
Smart lab
Future work
Conclusion
Reference
INTRODUCTION
Process of obtaining needed services, ideas, or content by
soliciting contributions from a large group of people, and
especially from an online community.
Distributed problem-solving model.
Crowd- sourcing in smart phones.
Extending existing web based crowd sourcing application to a
larger contributing client.
WHAT IS CROWDSOURCING?
Crowdsourcing is an online, distributed problem solving
and production model.
Users--also known as the crowd--typically form into
online communities based on the Web site, and the
crowd submits solutions to the site or produce its
contents.
The crowd can also sort through the solutions, finding
the best ones.
These best solutions are then owned by the entity that
broadcast the problem in the first place--the
crowdsourcer.
BENEFITS
Information can be collected quickly and efficiently
.
Crowdsourcing benefits firms by potentially
contributing significantly to innovation with careful
analysis.
Easy access to a global workforce with a wide
range of knowledge .
To exploit trajectory related information's .
PITFALLS
Research shows that crowdsourcing can favor
popular opinion which in turn favors homogeneity .
Crowdsourcing can be expensive.
Crowdsourcing can be unreliable.
Crowdsourcing requires no or little expertise from
participants.
No supervision of participants.
APPLICATIONS
Testing & Refining a Product
Netflix
Sella Band
CAPTCHA & RECAPTCHA
Knowledge Management
Wikipedia
Customer Service My Starbucks ideas
Polling and Voting
Smart phones
Airlines
Traffic System
CROWD SOURCING WITH SMART PHONES
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TABLE 1
Applications web-extend involvement data wisdom contrib. quality incentives human skill sensors location
Gigwalk.com X participatory individual heterogeneous monetary labor camera X
Jana.com X participatory individual heterogeneous monetary visual × X
Crowd Translator X participatory collective homogeneous service visual camera ×
Waze.com × both collective homogeneous ethical/service visual × X
City Explorer × participatory collective homogeneous entertainment visual camera X
V Track × opportunistic collective homogeneous ethical/service × × X
Signal Guru × opportunistic collective homogeneous ethical/service × camera X
Ear-Phone × opportunistic collective homogeneous ethical × audio X
Noise Tube × opportunistic collective homogeneous ethical × audio X
Potholes × opportunistic collective homogeneous ethical × vibration X
Air Place × opportunistic collective homogeneous service × × X
Smart Trace × opportunistic collective homogeneous service × × X
Crowd cast × opportunistic collective homogeneous service × × X
SmartP2P × opportunistic collective homogeneous service × × X
Taxonomy of Mobile Crowd sourcing Applications
TABLE 2
Basic Operation on Smartphone Power(mW=mJ/s)
CPU Minimal use (just OS running) 35mW
CPU Standard use (light processing) 175mW
CPU Peak (heavy processing) 469mW
WiFi Idle (Connected) 34mW
WiFi Localization (avg/minute) 125mW
WiFi Peak (Uplink 123Kbps, -58dBm) 400mW
3G Localization (avg/minute) 300mW
3G Busy 900mW
GPS On (steady) 275mW
OLED Economy Mode 300mW
OLED Full Brightness 676mW
Energy profiling of a typical Smartphone.
ISSUES AND CHARACTERISTICS OF
CROWDSOURCING WITH SMARTPHONES
Smartphones feature different Internet connection modalities
that provide intermittent connectivity .
Peer-to-peer connection capabilities that provide connectivity
to nodes in spatial proximity.
Centralized or decentralized.
Participatory or Opportunistic.
Localization.
APPLICATIONS OF CROWDSOURCING WITH
SMARTPHONES
Smarttrace
Crowdcast
Smartp2p
SMARTTRACE
Smart Trace
GUI enables the following functions:
i) Record and plot .
ii) Configure
iii) Connect to a SmartTrace+ server
iv) Switch between online and offline mode
(a) (b)
|Q|<<L
Query
Processor
QN
A1
A2
A3
LL
Q
(a) Our system model. (b) Screenshots of the SmartTrace+ client for
outdoor environments with GPS and indoor environments with RSS
signals
CROWDCAST
Mobile devices is to continuously provide k geographically
nearest neighbors in real-time.
Solves the Continuous All k-Nearest Neighbor (CAkNN)
problem efficiently.
Extended neighborhood “sensing” capability for mobile users
enables several novel applications.
CROWDCAST: SCREENSHOTS FROM AN EXAMPLE APPLICATION.
SMARTP2P
SmartP2P offers high performance search and data shar- ing
over a crowd of mobile users participating in a smartphone
social network.
Crowd to optimize the search process.
SmartP2P can be used as a recommender system where the
mobile social crowd .
(a) user enters a keyword of interest to issue a query
(b) The answer is returned back
(c) Decision Making process
(d) Fetches the selected optimized tree from a server
(e) User searches the peer-to-peer network
(f) Obtains a list of the results of interest.
THE FRAMEWORK WORKFLOW AND SCREENSHOTS OF THE SMARTP2P
CLIENT SIDE GUI IN ANDROID
SMART LAB
Smart- Lab12 test bed in order to implement and evaluate
smart- phone applications at a massive scale.
Smart Lab provides an open, permanent testbed for
development and testing ofsmartphone applications via an
intuitive web-based interface.
Registered users can upload and install Android executables
(APKs) on a number of Android smartphones, capture their
output, reboot the devices, issue commands and many other
exciting features.
FUTURE WORK
Crowd sourcing with smart phones will evolve rapidly in the
future.
Collection of specialized location-related data.
Energy consumption, privacy preservation and application
performance.
Extending the location-awareness.
CONCLUSION
Process of obtaining needed services, ideas, or content by
soliciting contributions from a large group of people, and
especially from an online community.
Smartphone networks comprise a new computation system
that involves the joint efforts of both computers and humans.
The unique data generated by the smartphone sensors and
the crowd’s constant movement.
The focus of future efforts in this area lies in the collection of
specialized location-related data.
REFERENCE
J. Ledlie, B. Odero, E. Minkov, I. Kiss, and J. Polifroni, “Crowd trans- lator: on building localized speech recognizers through micropayments,” ACM SIGOPS’10 Operating Systems Review.
A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrish- nan, S. Toledo, and J. Eriksson, “Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones,” in 7th Conference on Embedded Networked Sensor Systems (SenSys’09).
R. K. Rana, C.-T. Chou, S. Kanhere, N. Bulusu, and W. Hu, “Ear-phone: an end-to-end participatory urban noise mapping system,” in 9th International Conference on Information Processing in Sensor Networks (IPSN’10).
M. Stevens and E. D. Hondt, “Crowdsourcing of pollution data using smartphones,” Ubiquitous Computing (UbiComp’10).
J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakr- ishnan, “The pothole patrol: using a mobile sensor network for road surface monitoring,” in 6th international conference on Mobile systems (MobiSys’08).
Thank you…..
Any Questions????