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

10. Idea Due to the limitation of the transmission time, nodes should take both contact time and contribution into account Challenge: – Contribution – Contact duration Alice Bob Carol Daniel 10 Carol Contribution1.3 Duration1 Bob Contribution0.5 Duration2 Daniel Contribution1.8 Duration2

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 T tx (transmission time) A user can only forward the message to a single contact at a time 11 Goal: Distribute the message to as many users as possible before the deadline T max expires Goal: Distribute the message to as many users as possible before the deadline T max expires

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

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

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

15. Forwarding Scheduling Problem Backward induction algorithm – Run in pseudo-polynomial time O(δ|G i |) 15 Subject to: Whether user j can download the message at time t d ij tsts tete contribution = 0 j j contribution = Contribution of user j at time t

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

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

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 : = Similarity between two events g and g’ – Hamming distance between and 18 V 1 = V 2 = Similarity 12 = -2

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 19 New event d ij = ∑d ij (g) / |C 2 | Average duration between i and j in events belong to cluster C 2 C2C2 C1C1 C3C3 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 21

22. Accuracy of Contribution Estimation 22

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

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. 24

25. Receive nodes vs. Deadline 25 improve 145% coverage

26. Histogram of contribution of each user 26

27. Receive nodes vs. File size 27 101% 185% 3% 25% 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 28 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 29

30. PrefCast

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

32. A Naïve Solution Broadcast the object that maximizes the utility of local contacts – Suboptimal: Neglect the impact of future contacts (u 1,u 2 )=(10,5) (5,3) (3,10) (5,8) (3,8) A (2,10) B GAGA GBGB A B F u1u1 u2u2 A105 B53 Total158 u1u1 u2u2 A + G A 2033 B + G B 811 Total2844 Local contribution Global contribution Say the contact duration only allows F to broadcast 1 object To maximize local utility, the forwadrer should broadcast object 1 To maximize global utility, the forwarder should broadcast object 2

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? A (3,10) (5,8) (2,10) A GAGA ??

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

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 forwarder f encounters a group of contacts M in a set of available time-slots T Determine a forwarding schedule x m,t that maximizes preference contribution Subject to Global contribution of forwarding object m at time t 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] m1m1 m2m2 m3m3 m4m4 t1t1 t2t2 t3t3 Objects Time-slots ω g m4,t3 ω g m1,t1

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 ω g m,t

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

41. Estimating Future Utility Contribution Future contribution: U(i,m,t) – Duration between t and T expire – How may users that do not own object m have contacts with user B between (t,T expire ) – Preference of user B’s contacts for object m time B U(B,m,t) t T expire Contribute object m to other users between (t,T expire ) 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 NUSInfocomMITSLAW (synthetic model) No. of users500/2234 1 7897500 Duration77(hr)16(hr)35 (days)10(hr) Singers Acen1 Adriana Evans3 Air5 Bit Shifter6 Caro Emerald2 ……

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

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

49. The utility improvement increases when there are fewer users helping disseminate the object

50. Impact of Transmission Range The utility improvement increases when the forwarding distance is shorter

51. 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

52. Thank You