Participatory Sensing in Commerce: Using Mobile Phones to Track Market Price Dispersion

1. 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) UrbanSense08

2. 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 UrbanSense08

3. 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 UrbanSense08

4. 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 UrbanSense08

5. MobiShop System Architecture Request Internet GPRS/HSPDA/WiFi Response Central Server Matching Stores Upload analyzed text Product Search Query

6. 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 MobiShop vs. PetrolWatch MobiShop UrbanSense08

7. 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 validation UrbanSense08

8. 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 UrbanSense08

9. 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 UrbanSense08