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Content Dissemination in Mobile Social Networks

Content Dissemination in Mobile Social Networks. Cheng-Fu Chou. Content Dissemination in Mobile Social Networks. Users intrinsically form a mobile social network Ubiquitous mobile devices , e.g., smart phone Proximity-based sharing capability, e.g., WiFi , or bluetooth.

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Content Dissemination in Mobile Social Networks

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  1. Content Dissemination in Mobile Social Networks Cheng-Fu Chou

  2. Content Dissemination in Mobile Social Networks • Users intrinsically form a mobile social network • Ubiquitous mobile devices, e.g., smart phone • Proximity-based sharing capability, e.g., WiFi, or bluetooth 1. Opportunistically distribute content objects 2. Offload 3G/4G traffic

  3. Delay Tolerant Networks • DTN: • No network infrastructures • intermitted network connections • Unpredictable node mobility

  4. Unicast in DTN • Unicast routing • Constraint: buffer size, hop count, … • Existing works • Probability-based forwarding • Delivery probability • A. Lindgren, A. Doria, et al. "Probabilistic routing in intermittently connected networks," In Proc. SAPIR, 2004. • Social-based forwarding • Social properties, such as centrality and communities • E.M. Daly, M, Haahr, “Social network analysis for routing in disconnected delay-tolerant MANETs,” In ACM MobiHoc,2007

  5. Multicast in DTN • Multicast routing • Delivering to a set of given destinations • Goal: minimize delay, maximize delivery rate • W. Gao, Q. Li, et al. “Multicasting in Delay Tolerant Networks, A Social Network Perspective,” In ACM MobiHoc,2009

  6. Content Dissemination • Content Dissemination • No specific destinations • e.g., information broadcasting, content (audio/video) publishing • Distribute content to as many users as possible • Cellular Traffic Offloading [Bo Han et al. , CHANTS’10] • Offload cellular traffic through opportunistic communication • Focus on cellular communication target set selection

  7. Ours • DIFFUSE [TVT’11] • Single content diffusion in MSNs • Ad propagation or audio/video content dissemination • Different from related work • No specific destinations • Forward to as many users as possible • Transmission time is non-neglected • Unicast • PrefCast[Infocom’12] • Multi-content disseminations in a MSN • Satisfying all users’ preference as much as possible • Focusing on the content broadcasting strategy

  8. DIFFUSE

  9. Motivation User contribution: The number of useful contacts that the user can encounter after it becomes a forwarder those users who have high contact frequency may belong to the same community

  10. Idea Daniel Carol Alice Bob • Due to the limitation of the transmission time, nodes should take both contact time and contribution into account • Challenge: • Contribution • Contact duration

  11. Problem Definition and Assumptions • One source disseminates a single message • Relay node that can help propagate copy to those who have not received the message • Discrete model with the time-slot size Ttx(transmission time) • A user can only forward the message to a single contact at a time Goal: Distribute the message to as many users as possible before the deadline Tmaxexpires

  12. Motivating Example 1 Candidate receivers A Relay node Contact duration (relay, receivers) B C C B A → A B C Select the receivers that have the shortest contact duration first Contact users with different contact duration

  13. Motivating Example 2 Candidate receivers A Contribution: A: 1.2 B: 0.9 C: 0.5 Relay node B C C B A → A B C Select the receivers that have the largest contribution first Contact users with the same contact duration, yet different contributions

  14. Motivating Example 3 Candidate receivers Contribution: A: 1.3 B: 0.9 C: 0.5 A Relay node B C C B A A B C X → C B A Take both contact duration and contribution into account Contact users with different contact durations and contributions

  15. Forwarding Scheduling Problem • Backward induction algorithm • Run in pseudo-polynomial time O(δ|Gi|) Contribution of user j at time t Whether user j can download the message at time t dij Subject to: j j ts te contribution = contribution = 0

  16. Backward Induction Algorithm Candidate receivers Contribution: A: 0.5 B: 0.2 C: 0.7 D: 0.2 E: 0.4 A B Relay node C D E EA C X X B B C A E → E A C B

  17. Estimate of contribution • Duration between t and Tmax • How many users that do not own object m have contacts with user B between (t,Tmax)

  18. Estimate of contact duration • Motivation: Average contact duration is too rough • The duration of a contact is correlated to the event that they join • Characterize each event g by a vector : = <b1, b2,…,bk,…> • Similarity between two events g and g’ • Hamming distance between and V1 = <01100> Similarity 12 = -2 V2 = <01001>

  19. Estimate of contact duration • Contacts in two events are more likely to have the same duration if these events are composed of the same subset of users • Cluster-based estimation C2 dij = ∑dij(g) / |C2| New event Average duration between i and j in events belong to cluster C2 C1 C3 History events that include i and j

  20. Performance

  21. Performance Evaluation • Experiment Setting • Real trace from class schedule of University of Singapore • Bluetooth with the throughput 128kbps • One randomly selected source that transmits a file with the size 30MB • Evaluation • Accuracy of contribution and contact duration estimation • Performance of DIFFUSE

  22. Accuracy of Contribution Estimation

  23. Accuracy of Duration Estimation • CDF of Estimation Error 31% 84% 49% 74%

  24. Comparison schemes • Oracle • Contribution: number of users that have not got the copy in the system • Exact contact duration • Epidemic • each relay node randomly selects a contact as the receiver at each time-slot • A. Vahdat and D. Becker, “Epidemic Routing for Partially Connected Ad Hoc Networks,” Technical Report CS-200006, Duke University, Tech. Rep., 2000. • PROPHET • estimate the probability of contact between a relay and the destination • A. Lindgren, A. Doria, et al. Probabilistic routing in intermittently connected networks. In Proc. SAPIR, 2004.

  25. Receive nodes vs. Deadline improve 145% coverage

  26. Histogram of contribution of each user

  27. Receive nodes vs. File size 3% 25% 101% 185% It becomes more important to select receivers when transmission time becomes long because only few contacts can get the copy

  28. Percentage of the groups with relay node Our scheme can disseminate the copy to more different groups

  29. Conclusions • Propose a backward induction algorithm for content diffusion in MSNs • Consider the impact of contribution and contact duration, and provide prediction metrics • Achieve better delivery ratio than Epidemic and PROPHET, even close to the solution with oracle information

  30. PrefCast

  31. Existing Dissemination Protocols Speed up content dissemination • without considering heterogeneous user preferences for various content objects PrefCast A content dissemination protocol that maximally satisfies user preference

  32. A Naïve Solution To maximize local utility, the forwadrer should broadcast object 1 To maximize global utility, the forwarder should broadcast object 2 • Broadcast the object that maximizes the utility of local contacts • Suboptimal: Neglect the impact of future contacts Say the contact duration only allows F to broadcast 1 object GA (3,10) (u1,u2)=(10,5) A (2,10) A F (5,8) (5,3) GB B (3,8) B Globalcontribution Local contribution

  33. Our Goal • Take future contribution into account • How to predict future contribution? • Broadcast the objects of interest within limited contact duration • Given future contribution estimation, how to find the optimal forwarding schedule

  34. 1. How to Predict Future Contribution? • How many future contacts can be encountered by its current contact • How to know the preference of those future contacts? (3,10) (2,10) (5,8) ?? GA A A

  35. 2. How to Find the Forwarding Schedule? • Each contact has a different contact duration D A Intuitively, should give a contact with a short contact duration a higher priority F B E C E D C B A time Transmission time of one object

  36. Take future contribution into account • How to predict future contribution? • Utility contribution estimation • Broadcast the objects of interest within limited contact duration • Given future contribution estimation, how to find the optimal forwarding schedule • Optimal forwarding scheduling algorithm

  37. Maximum-Utility Forwarding Model When a forwarderfencounters a group of contacts Min a set of available time-slotsT Determine a forwarding schedule xm,t that maximizes preference contribution Global contribution of forwarding object m at time t Subject to Single item per time slot Broadcast once per object

  38. Maximal Weight Bipartite Matching • Constraint 1: Each time-slot can only connect to an object • Constraint 2: Each object can only be assigned one time-slot • Any bipartite matching is a feasible solution • The total utility contribution equals the weight of the matching • Maximum utility = Maximal weight bipartite matching • Solved by the Hungarian algorithm[Kuhn-NRLQ’55] m1 m2 m3 m4 Objects ωgm1,t1 ωgm4,t3 Time-slots t1 t2 t3

  39. Take future contribution into account • How to predict future contribution? • Utility contribution estimation • Broadcast the objects of interest within limited contact duration • Given future contribution estimation, how to find the optimal forwarding schedule • Optimal forwarding scheduling algorithm ωgm,t

  40. Estimating Global Utility Contribution Future contribution that i can generate if it gets object m at time t Vτ={A, B, C, D, E} A already has object m C and D leave before time-slot t wgm,t=U(B,m,t) +U(E,m,t) E U(E,m,t) D C B U(B,m,t) A time

  41. Estimating Future Utility Contribution • Future contribution: U(i,m,t) • Duration between t and Texpire • How may users that do not own object m have contacts with user B between (t,Texpire) • Preference of user B’s contacts for object m time U(B,m,t) B Texpire t Contribute object m to other users between (t,Texpire) Computed by neighbor B  Forwarder makes decision in a distributed manner

  42. Performance

  43. Simulation Settings • Traces • User preference profile • Last.fm • 8,000 users • 100 favorite songs • Classify by singers

  44. Cumulative Utility • PrefCast • Local Utility • Epidemic Routing (a) NUS (b) infocom (c) MIT (d) SLAW

  45. Cumulative Utility Improve the average utility by ~25% (a) NUS (b) infocom (c) MIT (d) SLAW

  46. Impact of Number of Users SLAW

  47. Impact of Number of Users

  48. Impact of Number of Users

  49. Impact of Number of Users The utility improvement increases when there are fewer users helping disseminate the object

  50. Conclusions • PrefCast: Distributed preference-aware content dissemination protocol for mobile social networks • Optimal forwarding scheduling model • Prediction of the future contributions • Shown utility improvement via real traces and synthetic traces

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