On Exploiting Transient Social Contact Patterns

for Data Forwarding in Delay-Tolerant Networks

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Wei GaoGuohong CaoTom La PortaJiawei Han

Presented by Michael Conlan

AgendaIntroductionOverview of Network Model and AlgorithmTrace-Based Pattern FormulationData Forwarding MetricExploiting Transient Community StructurePerformance EvaluationConclusion

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 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

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 λt of node C would be 2.83 ((2x1+3x5)/6) and C would look better than A

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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

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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

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Pattern Formulation: Transient Contact Distribution

For Ton set to 8 hrs, results are:

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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

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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

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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

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Joint period can be also accurately approximated by normal distribution Note μco is less than μon

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

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

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

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Forwarding Metric: Contact Probability

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The contact process is stable and predictable only during on-periods as in case 1(a) and 2 (b)

Contact occurrence probabilitypij=pij

1+pij2

Probability of contact during on-periodpc(t1,t2)=1-e-λ(t2-t1)

Forwarding Metric: Contact ProbabilityCase 1 Case 2

Forwarding Metric: Incorporating Transient Connectivity

Case 1 Case 2

Incorporate TCS where size given by:

pij from last page but now:

Similar transformation

finally,

Forwarding Metric:Prediction Error

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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

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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

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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 Ton

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Ton of 8 hrs has optimal performance Smaller time contraint is more sensitive

to sub-optimal Ton

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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

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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

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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