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)

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Query

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(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????