The MobiSoC Middleware for Mobile Social Computing

1. The MobiSoC Middleware for Mobile Social Computing CristianBorcea, Ankur Gupta, AchirKalra, Quentin Jones, LiviuIftode* Department of Computer Science New Jersey Institute of Technology *Rutgers University

2. Social Computing in the Internet • Social networking applications that improve social connectivity on-line • Stay in touch with friends • Make new friends • Find out information about events and places Myspace Facebook LinkedIn

3. Shift from Physical Communities to Virtual Communities • Leads to missed social opportunities • People not aware of their neighborhoods • Example: don’t know neighbors with common interests or nearby events • Inter-personal affinities can be leveraged in stronger social ties in physical communities • People who share common places can easily meet and talk • Is there any way to get the best of both worlds? • Merge the benefits of social computing and physical communities?

4. Mobile Social Computing • Social computing anytime, anywhere • New applications will benefit from real-time location and place information • Smart phones are the ideal devices • Always with us • Internet-enabled • Locatable (GPS or other systems) • 200-400 MHz processors • 64-128 MB RAM • GSM, WiFi, Bluetooth • Camera, keyboard • Symbian, Windows Mobile, Linux • Java, C++, C#

5. Are People Willing to Share their Location? • Yes, if they benefit from that • Study with 500+ people in Manhattan over 3 weeks • 84% willing to share location to compute place crowding • 77% willing to share their location data with others in public or semi-public places • 57% would like to know information about other people

6. Mobile Social Computing Applications (MSCA) • People-centric • Are any of my friends in the cafeteria now? • Is there anybody nearby with a common background who would like to play tennis? • Place-centric • How crowded is the cafeteria now? • Which are the places where CS students hang out? • How to program MSCA? • Challenges: capturing the dynamic relations between people and places, location systems, privacy, power

7. Outline • Motivation • MobiSoC Middleware • Applications • Clarissa: people-centric MSCA • Tranzact: place-centric MSCA • Implementation & experimental results • Conclusions

8. MobiSoC Middleware • Common platform for capturing, managing, and sharing the social state of a physical community • Discovers emergent geo-social patterns and uses them to augment the social state

9. MobiSoC Architecture

10. Learning Emergent Geo-Social Patterns Example: GPI • GPI – algorithm that identifies previously unknown social groups and their associated places • Fits into the people-place affinity learning module • Clusters user mobility traces across time and space • Its results can • Enhance user profiles and social networks using newly discovered group memberships • Enhance place semantics using group meeting times and profiles of group members

11. Location System • Hardware-based location systems not feasible • GPS doesn’t work indoors • Deploying RF-receivers to measure the signals of mobiles is expensive and not practical for large places • The user has no control over her location data! • Software-based location systems that run on mobile devices preferable • Use signal strength and known location of WiFi access points or cellular towers • Allow users to decide when to share their location

12. Mobile Distributed System Architecture • MSCA split between thin clients running on mobiles and services running on servers • MSCA clients communicate synchronously with the services and receive asynchronous events from MobiSoC • Advantages • Faster execution • Energy efficiency • Improved trust

13. Clarissa: Location-enhanced mobile social matching MatchType=Hangout Time: 1-3PM Co-Location: required Match Alert Match Alert MatchType=Hangout Time: 2-4PM Co-Location: required

14. Tranzact: Place-based ad hoc social collaboration Hungry What’s on the menu? Chicken teriyaki Cafeteria

15. MobiSoC Implementation • Runs on trusted servers • Service oriented architecture over Apache Tomcat • Core services written in JAVA • API is exposed to MSCA services using KSOAP • KSOAP is J2ME compatible, hence can be used to communicate with clients • Client applications developed using J2ME on WiFi-enabled Windows-based smart phones • Clarissa: http://apps.facebook.com/matching/ • Location engine: modified version of Intel’s Placelab • At least 3 WiFi access points visible in most NJIT places • Accuracy 10-15 meters

16. Location Engine Power Consumption • Trade-off between frequent location updates for synchronous awareness and rare updates to save power

17. Experimental results Mobility traces from 20 users carrying smart phones over one month period Identified all groups and places (place accuracy < 10 meters) Simulations for larger scale Identified over 96% of members, when meeting attendance frequency at least 50% Less than 1% false positives GPI Results

18. Conclusions • Mobile social computing applications can be deployed in real-life today • MobiSoC manages community social state • Discovers emergent patterns from social interactions • Improves people and place profiles using these patterns • Provides support for rapid application development • Distributed system architecture based on MobiSoC addresses efficiency, power, and trust issues • SmartCampus: large scale mobile social computing test-bed at NJIT • Test mobile social computing applications with 200+ users carrying smart phones across the campus this spring

19. Thank you! Work sponsored by the NSF grants CNS-0454081, IIS-0534520, CNS-0520033, and CNS-0520123 http://www.cs.njit.edu/~borcea/