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

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

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?

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

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

Overview: Forwarding Algorithm  Forwarding decision of whether node i sends data, d k, to node j dependent on node forwarding metrics and forwarding strategy  m j = data forwarding metric of node j  Q i = strategy based metric of i to compare with m j  Common strategy that forwards data d k to node j if:  Node j is the destination node  Else if node j is in the community of the destination node and node i is not  Else if Q i < m j  Calculate m based on transient contact perspectives

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

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 λ t 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

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

11 Pattern Formulation: Transient Contact Distribution For T on set to 8 hrs, results are:

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

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 

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. 

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

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

Pattern Formulation: Transient Community Structure 17  Joint period can be also accurately approximated by normal distribution  Note μ co is less than μ on  Low variance indicates large communities

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  C i is node i's centrality calculated by the sum of c ij, 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

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

Forwarding Metric: Update Algorithm 20

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

Forwarding Metric: Contact Probability Case 1 Case 2

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

Forwarding Metric:Prediction Error 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

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

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

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

Performance Comparisons – Data Delivery Ratio 28  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

Performance Comparisons – Forwarding Costs  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: Impact of T on 30  T on of 8 hrs has optimal performance  Smaller time contraint is more sensitive to sub-optimal T on

31 Performance: Impact of Transient Connectivity  Transient metric C i 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

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

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

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

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

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