Social networks adding community-scale to context-aware connectivity management

People are accessing online social networks wherever they go through smartphones and tablets. These mobile devices are capable to sense, compute and communicate, allowing people to create and consume rich digital content anywhere. Popular social applications are attaching geographical localization to user-generated digital content, creating geo-tagged social media. Given the heterogeneity of current wireless environments (i.e.,
multiple access providers and communication technologies) it is challenging to keep mobile devices best connected anywhere. In this paper, a wireless connectivity manager is designed as a sensing system. The mobile device’s wireless interfaces are the sensors and the collected context data is shared attached to geo-tagged social media. The goal was take advantage of popular location-based web applications to deliver connectivity context data within social circles. As part of a sensing system, online social networks adds scale to the system and allow collaboration around fresh, local, personalized and social context data. Simulations were performed to quantify how collaboration evolves, to discover connectivity opportunities in a specific place,
as function of community size and users mobility patterns.

Transcript

1. 1 WCNC 2013 WCNC 2013 Social networks adding community-scale to context-aware connectivity management Roberto Rigolin Ferreira Lopes (robertorigolin@gmail.com) Department of Engineering Cybernetics – ITK Norwegian University of Science and Technology –NTNU Shanghai, 09 April 2013
2. 2 WCNC 2013 Agenda • Introduction • Background • Sensing and sharing • Using connectivity context data • Conclusion
3. 3 WCNC 2013 Introduction • People are using their smartphones and tablets to post and access location-based and time-specific information in social networks • Transforming social systems in repository of sensed data px Sensor Repository Picture Localization Mesage Geo-tagged social media
4. 4 WCNC 2013 Introduction • People’s connectivity experiences evolve as function of three main areas, the axis above: Hardware and wireless technologies Wireless connectivity management Social applications W ireless connectivity Social circles
5. 5 WCNC 2013 Introduction • We discuss the feasibility of a system to explore geo-tagged social media to share context data used by recent wireless connectivity managers Net 1 Net 2 Net 3 Mobile User Device Broadcasted information Picture Localization Mesage Connectivity Geo-tagged social media +
6. 6 WCNC 2013 Background • Connectivity management: ideal algorithm Wireless interface on Is associated to an AP? No Connected Moving? Yes Find another AP Reach the border? Yes No Yes Got an IP? Yes Predict user mobility Choose the next AP Association
7. 7 WCNC 2013 Background • Connectivity management: ideal algorithm implementation Wireless interface on Is associated to an AP? No Connected Moving? Yes Find another AP Reach the border? Yes No Yes Got an IP? Yes 1 2 3 Mobility Manager Handover Mechanism Mobility Predictor Historical Context Database Keep the connection(s) alive. Predict the movement. Choose the next AP. <sensed data>
8. 8 WCNC 2013 Background • Geo-tagged social media + ideal connectivity algorithm Mobility Manager Handover Mechanism Mobility Predictor Historical Context Database Geo-tagged social media ... People in a social circle Online social network(s) At the cloud Connectivity management Picture Localization Mesage Connectivity Sensors: camera, GPS, wireless interface(s)... 1 3 2 republish/consume
9. 9 WCNC 2013 Background • Continuous sensing – Scale: personal, group and community – Engagement: opportunistic, hybrid and participatory Scale Engagement Community Personal Opportunistc Participatory Mobile crowdsensing Hybrid (i) Group (ii) Social networks Connectivity management px Sensor
10. 10 WCNC 2013 Sensing and sharing • Scenario – A navigation application will be able to retrieve connectivity context data while the user is moving, attached to geo-tagged social to manage the device’s wireless connectivity. px WiFi connected LTE connected Logged in. Logged in. Connectivity manager Navigation app. User’s RSS feeds <localization> Multi-homed Connected to more than one social system
11. 11 WCNC 2013 Sensing and sharing • At the mobile device – The wireless connectivity manager runs seamless to the others applications in a sensing loop: • Sensing connectivity context • Check errors/missing data • Align data to the user’s privacy Sense Analytics Privacy Operational system Sensors: camera, GPS, wireless interface(s)... Connectivity manager Picture Localization Connectivity Application(s) input
12. 12 WCNC 2013 Sensing and sharing • At the cloud: sensing loop Sense Analytics Privacy Location-based service(s) publish search post (B) At the cloud search <place> <map> <nick><name> <date><time> <mashup> //localization <place> <map> //mobile user <nick><name> <date><time> //context data <connectivity> <\mashup> mashup scheme 3 4 share combine output: location-based social media input (A) (C) (D) Delivery system Operational system Sensors: camera, GPS, wireless interface(s)... Context manager Connectivity manager Picture Localization Connectivity 1 2 Application(s) Online social network(s) Localization input
13. 13 WCNC 2013 Using connectivity context data • Experimental results: three connectivity mechanisms – Strongest Signal Strength (SSS) – Mobility predictor (Predictor) – Social-based (Social) • The social mechanism experienced throughput ~14 and 23 % better than the other two, during three months,
14. 14 WCNC 2013 Using connectivity context data • Experimental results – Inverse relation between the scan intervals and power consumption to discover connectivity opportunities. – With small scan intervals the device can discover more connectivity opportunities, however, it also consumes more battery
15. 15 WCNC 2013 Using connectivity context data • Experimental results
16. 16 WCNC 2013 Using connectivity context data • Simulations setup – Tools: OMNet++, the Veins framework and SUMO (Simulation for Urban Mobility) – Communities of mobile users, with sizes varying from 16 to 1024 users, moving in the network environment Public WiFi network in Portland, USA. 72 access points and 200k geo- tagged scans
17. 17 WCNC 2013 Using connectivity context data • Simulations setup – Tools: OMNet++, the Veins framework and SUMO (Simulation for Urban Mobility) T. Fujisaka, R. Lee, and K. Sumiya. Discovery of user behavior patterns from geo- tagged micro-blogs. In ICUIMC ’10, pages 36:1–36:10, New York, NY, USA, 2010. ACM. Majors cities in Japan and South Korea: 359,709 posts from 8,139 users
18. 18 WCNC 2013 Using connectivity context data • Mobility patterns – Low ~8 km/h – Medium ~25 km/h – High ~40 km/h Low mobility: walking High mobility: driving
19. 19 WCNC 2013 Using connectivity context data • Simulation results – Map the connectivity opportunities depends on the # users collaborating and the area size – Simulation time: 8 hours – The curve fits the data with 80% of confidence 512, ~8 Km/h 128, 25 Km/h 32, 40 Km/h
20. 20 WCNC 2013 Using connectivity context data • Simulation results – # geo-tagged posts 512, ~8 Km/h 128, 25 Km/h 32, 40 Km/h
21. 21 WCNC 2013 Conclusion • The goal was add scale and relevance to connectivity context data combining it with geo-tagged social media • Usually, people combines different mobility patterns in dally life. A future work is employ mobility models extracted from geo-tagged social media Hardware and wireless technologies Wireless connectivity management Social applications W ireless connectivity Social circles
22. 22 WCNC 2013 The end. Social networks adding community-scale to context- aware connectivity management Roberto Rigolin Ferreira Lopes robertorigolin@gmail.com Department of Engineering Cybernetics – ITK Norwegian University of Science and Technology –NTNU