Participatory Sensing in Commerce: Using Mobile Phones to Track Market Price Dispersion Nirupama...
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Transcript of Participatory Sensing in Commerce: Using Mobile Phones to Track Market Price Dispersion Nirupama...
Participatory Sensing in Commerce: Using Mobile Phones to Track Market Price Dispersion
Nirupama Bulusu (Portland State University)
Chun Tung Chou, Salil Kanhere, Yifei Dong,
Shitiz Sehgal, David Sullivan and Lupco Blazeski
(University of New South Wales, Australia) 11/08/2008UrbanSense08
Price Dispersion “The empirical evidence for
price dispersion in both online and offline markets is sizeable, pervasive and persistent” (Baye et al, Handbook of Economics and Information Systems, 2006)
Attributed to “shoe leather” costs
11/08/2008UrbanSense08
Today Numerous on-line price
comparison sites Shopzilla, Amazon, Froogle Information extraction from web
databases easy to automate Price comparison sites for off-line
markets too Prices from grocery shops manually
copied in Hong Kong Petrol prices collected by volunteers
or web site staff in US, UK, Australia Manual collection is cumbersome,
error-prone and not up-to-date
11/08/2008UrbanSense08
Participatory Sensing to Track Price Dispersion Harness power of the collective via participatory
sensing Consumers collect and share pricing information Design criteria:
As automated as possible to reduce reluctance in participation
Use camera phones to replace human sensing, processing and communication tasks
Two proof-of-concept systems to demonstrate feasibility MobiShop: Automated product price collection PetrolWatch: Automated fuel price collection
11/08/2008UrbanSense08
MobiShop System Architecture
Central Server
GPRS/HSPDA/WiFi
Upload analyzed text
Request
Response
Internet
Product Search Query
Matching Stores
MobiShop vs. PetrolWatch Nearly identical system architectures PetrolWatch – camera position important
Special computer vision algorithms for extracting fuel price information (on server/camera phone) Use of GPS and GIS to simplify image processing
PetrolWatch MobiShop11/08/2008UrbanSense08
Open Problems Data integrity
Bad data discourages users, reputation ranking methods could compromise privacy and anonymity
Privacy Statistical data perturbation, fudging data resolution etc.
won’t suffice since individual data items are of interest here
Anonymity Require information flow to server without revealing
identity Integrity, privacy and anonymity concerns are
potential barriers to participation Incentive mechanism requires larger scale studies for
validation11/08/2008UrbanSense08
Related Work Mobile phones in e-commerce
Rural microfinance (CAM) Fair trade (Reuters Market Light)
Agricultural price dissemination to farmers
Sensor Data Clearinghouses SensorMap, SensorBase
Participatory Sensing Systems DietSense, TrafficSense, BikeNet, Cartel etc.
Security and Privacy for Participatory Sensing AnonySense, PoolView, Participatory Privacy
Regulation
11/08/2008UrbanSense08
Conclusion Participatory Sensing to Track Market Price
Dispersion Two proof-of-concept systems: PetrolWatch and
MobiShop Addressed challenge of collecting non-structured
information Addressed usability, cost barrier to participation
Opportunities/Challenges Data Integrity, User privacy and Anonymity Tackling Other Barriers to Participation Through
Incentives Augmentation of Geographic Information Systems
11/08/2008UrbanSense08