On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks

1. On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay-Tolerant Networks Wei Gao Guohong Cao Tom La Porta Jiawei Han Presented by Michael Conlan 1

2. Agenda Introduction Overview of Network Model and Algorithm Trace-Based Pattern Formulation Data Forwarding Metric Exploiting Transient Community Structure Performance Evaluation Conclusion

3. Introduction: Problem Statement Delay Tolerant Networks (DTN) populated by mobile devices have intermittent connectivity and low node density Data forwarding metrics determined by stochastic processes and predicted node mobility limited by human randomness Problem Statement: How best to forward/relay data in DTNs to ensure timely and efficient delivery?

4. Introduction: Social Contact Patterns Node forwarding capability characterized by their Social Contact Patterns: Centrality – connectivity to many nodes that enables wider or faster delivery Community – naturally occurring grouping of connected nodes Consider on global scope and local scope Most social aware forwarding schemes based on cumulative social contact patterns BUT cumulative contact patterns differ from transient contact patterns

5. Introduction: Proposed Solution Proposed Solution: Exploit Transient Social Contact Patterns to improve data forwarding by considering these perspectives of contact patterns: Transient Contact Distribution – rate of contacts over time Transient Connectivity – formation of transient connected subnets (TCS) for periods of time Transient Community Structure – different communities created through the day Show that these perspectives have predictable behavior representable by Gaussian functions Develop forwarding metrics based on these functions to use in a forwarding strategy for better data delivery

6. Overview: Forwarding Algorithm Forwarding decision of whether nodei sends data, dk, to nodej dependent on node forwarding metrics and forwarding strategy mj = data forwarding metric of nodej Qi = strategy based metric of i to compare with mj Common strategy that forwards data dk to nodej if: Nodej is the destination node Else if nodej is in the community of the destination node and nodei is not Else if Qi < mj Calculate m based on transient contact perspectives

7. Overview: Transient Perspectives These perspectives provide more accurate estimation on the node's capability of contacting others within a given scope and time period Fig (a) shows that λ, rate of contacts, varies over time and transient values provide greater fidelity than cumulative Fig (b) shows how B may be a better choice than C due to indirect access to more nodes despite a lower contact rate

8. Overview: Transient Perspective Rates are further refined by considering scope over time Rate is weighted higher when node is in a community local to the destination node For example, if the transient community structure of C is not considered, then λt of node C would be 2.83 ((2x1+3x5)/6) and C would look better than A

9. Trace-Based Pattern Formulation Performed study on multiple networks to understand and characterize their transient contact patterns Network model and assumptions include Contacts are symmetric Stochastic contact process modeled as edge Data is small such that bandwidth and buffering are considered irrelevant -Bluetooth devices detect peers nearby and make contact to them -WiFi search access points (AP) and make contact with others on same AP 9

10. Pattern Formulation: Transient Contact Distribution On-period of length Lon is when there are a set of contacts within a threshold time Ton Stable and predictable on-periods 10

11. Pattern Formulation: Transient Contact Distribution For Ton set to 8 hrs, results are: 11

12. Pattern Formulation: Transient Contact Distribution Graphs show that the distribution of on and off periods can be accurately approximated by normal distribution using mean and variance below Model validated by mean on and off adding to 24 hrs, and >80% of contacts occur during on-periods 12

13. Pattern Formulation: Transient Connectivity A node's connectivity is represented by the size of it's TCS (Transient Connected Subnet) The TCS of node i during time period [t1,t2] consists of all nodes that have end to end comms with node i during that period 13

14. Pattern Formulation: Transient Connectivity TC depends on distribution of contact duration. MIT: 20% > 1hr, UCSD: 30% > 1hr, Infocom: 5% > 30mins. The average TCS size of each node. MIT: over 50% > 3, UCSD: over 50% > 100, Infocom: negligible due to 30min issue above. 14

15. Pattern Formulation: Transient Connectivity • The average TCS of all nodes can be approximated by: * A = amplitude function • Fig 3 & Fig 9 correlate therefore demonstrate that TC is proportional to the amount of contacts during time period t

16. Pattern Formulation: Transient Community Structure Community structure only exists if there are more nodes than a certain threshold that form a stable community Community relationship defined as a “joint-period” when a pair of nodes are in the same community Detection of communities by k-clique and modularity method Fig. 10 shows low community change at peak of node contacts (see Fig. 3 ) when community is stable and at night when only few contacts occur

17. Joint period can be also accurately approximated by normal distribution Pattern Formulation: Transient Community Structure • Note μco is less than μon • Low variance indicates large communities 17

18. Forwarding Metric Data forwarding metric based on node centrality Measure node centrality for a given scope and time constraint using transient contact distribution and transient connectivity • Ci is node i's centrality calculated by the sum of cij, the number of nodes i can contact by contacting j • Direct contacts determined from transient contact distribution • Indirect contacts through j based on transient connectivity of node j

19. Forwarding Metric:Incorporating Transient Contact Pattern For each pair of nodes i and j, the parameters of their on-period and off-period are updated every time they directly contact each other Each node detects its TCS when contacted by broadcasting a detecting beacon Transient connectivity is then updated by Gaussian curve fitting based on the recorded TCS sized during different hours

20. Forwarding Metric: Update Algorithm 20

21. The contact process is stable and predictable only during on-periods as in case 1(a) and 2 (b) Contact occurrence probability pij=pij1+pij2 Probability of contact during on-period pc(t1,t2)=1-e-λ(t2-t1) Forwarding Metric: Contact Probability 21

22. Forwarding Metric: Contact Probability Case 2 Case 1

23. Forwarding Metric: Incorporating Transient Connectivity Case 2 Case 1 Incorporate TCS where size given by: Similar transformation pij from last page but now: finally,

24. Contact occurs but not known to be the start of an on-period or still an off-period But 80% of contacts occur during on-period according to previous results Long off periods lower accuracy of Case 2 Forwarding Metric:Prediction Error 24

25. Exploiting Community Structure As in case (b) above, the forwarding metric is weighted by community membership over time Network periodically detects community members Can incorporate joint-period statistics 25

26. Performance Comparison: Setup Used the test networks and randomly picked source and destination nodes Social contact patterns are characterized real time as described Community structure measured by modularity method Performance criteria are data delivery ratio and forwarding cost

27. Performance Comparison: Setup Compared with other forwarding metrics: Contact counts (CC)-calculated cumulatively since network start Betweenness-social importance of a node facilitating communication among others Cumulative contact probability (CCP)-prob of contacting others based on cumulative contact rates Forwarding Strategies used Compare-and-forward-forward to all nodes with higher metric than itself Delegation forwarding-forward to all nodes with higher metric than the highest it has ever had Spray-forward limited set of copies to nodes with highest metric, each relay node forwards one copy to highest Epidemic is the benchmark and BUBBLE Rap also tested

28. Performance Comparisons – Data Delivery Ratio • Using compare and forward strategy with different metrics • When time constraint is short, transient approach far outperforms all others and matches epidemic • With a longer time constraint, the cumulative characteristics become more consistent and transient advantage decreases 28

29. Graphs show transient metric results in 20% lower forwarding cost General uptrend with increasing time constraint since more time allows more to be forwarded nodes to Performance Comparisons – Forwarding Costs

30. Ton of 8 hrs has optimal performance Smaller time contraint is more sensitive to sub-optimal Ton Performance: Impact of Ton 30

31. Performance: Impact of Transient Connectivity Transient metric Ci is calculated considering direct contacts only Performance still better at lower time constraint, worsens at higher time Delta performance across networks due to large TCS size in USCD and short contact duration in Infocom 31

32. Performance: Case 1&2 Contact Prediction Case 1 predicts an on-period will continue and contributes more to delivery with low time constraint Case 2 predicts future on-periods and has more accuracy with a longer time constraint 32

33. Performance: Static Community Structure Using a static community structure shows decreased delivery with a high time constraint With low time constraint, most delivery must be local anyway 33

34. Performance: Community detection Comparison of community detection methods show little difference in cost but better performance with modularity

35. Performance: Transient Community Structure with Different Forwarding Strategies • Delegation best overall with near max performance and lower cost since it's forwarding is more selective • Spray has limited cost and limited performance due to limited node

36. Conclusion Transient social contact patterns are an effective way to determine a forwarding metric Demonstrated predictive behavior of social contact patterns Developed transient forwarding metric based on transient social contact pattern parameters Evaluated forwarding performance and showed improved performance over static methods