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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. Agenda. Introduction Overview of Network Model and Algorithm Trace-Based Pattern Formulation - PowerPoint PPT Presentation
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On Exploiting Transient Social Contact Patterns
for Data Forwarding in Delay-Tolerant Networks
11
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
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