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

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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

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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

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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

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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

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