اصفهان صنعتي دانشگاهكامپيوتر و برق دانشكده

Social Network Analysis for Routing in DisconnectedDelay-Tolerant MANETs

مهدی ابوالفتحی

ارائه مقاله تحقيقي در درس” شبكه هاي مخابرات بي سيم “

مدرس: دکتر جمال الدين گلستانی

1387-1386نيمسال بهار

Contents

• Delay-Tolerant Networks (DTNs)• Related Works (Prophet, Epidemic)• Motivations• Proposed SimBet• SimBet Utility• Simulation Results• Conclusions

What is a DTN?

● Disconnection (Predictable or Random)● High latency, low data rate● Longer queuing time● Longer round-trip time● Limited resources

Ad Hoc DTNs

● Node density is low● Contacts between the nodes do not

occur very frequently● Rarely connected● Nodes rely on other nodes to relay

packets exploiting mobility● Mobility of nodes is unknown in advance

and may change over time

Routing in DTNs

Before DTNs:

• Space dependency

•Network as a given graph G(V, E), find shortest path between source and destination

•Store-and-forward routing

After DTNs:

• Space and time dependency

• Network as a time-varying graph G(V, E(t))

• Links are a function of time

• Store-carry-and-forward routing

Related Works

• Deterministic– Assumes node movements are deterministic

• Epidemic [38]- Expensive in terms of resources

• Prophet [24] - Probability-Based, Past encounters

Introduction and Motivation

• Routing in a disconnected network graph– Traditional MANET Routing protocols fail– Store-carry-forward model used– Global view of network unavailable and

volatile

• Social Networks– Milgram’s ‘Small world’– Hsu and Helmy’s analysis of wireless

network

• Epidemic routing– Each node maintain a buffer for messages– Simply flood messages when meeting– Pros:

• Always find the best possible path to destination

• Guarantee delivery• Minimal end-to-end delay

– Cons:• Flooding number of transmissions• Multiple copies amount of buffer space

Epidemic Routing

• PROPHET Routing– Probability-based– Using past encounters to predict the future– Exchange encounter probabilities when

meeting– Pro:

• One copy save space and number of forwards

– Con:• Low connectivity Fail!

PROPHET Routing

SimBet

• Metric comprised of both a node’s centrality andsocial similarity.• For unknown destinations, message routed to a‘more central’ node to increase potential of findingsuitable carrier.

• No assumptions of: – Node future movement control – Message multi-copies ( leading to a conservation of network resources).

• Improves on encounter-based strategies wheredirect or indirect encounters may be available.

Solution

• Exploit Social Network Analysis Techniques in order to:– Identify bridging ties

• Centrality

– Identify clusters• Similarity

Centrality Metrics [Freeman 1977,1979]

● Degree centrality– popular nodes in the network

● Closeness centrality– the distance of a given node to each node in the

network

● Betweenness centrality– the extent to which a node can facilitate

communication to other nodes in the network

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Ego Network Centrality Measures

• Analysis of a node’s local neighbourhood

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Egocentric Betweenness Correlation

Node Sociocentric

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• Node contacts represented in symmetric adjacency matrix

if there is a contact between i and j

otherwise

• Ego betweenness is given as the sum of the reciprocals of

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Betweenness Utility Calculation

Similarity

• Measured as the number of common neighbors between individual nodes

• similarity of social circles

• used to predict future interactions

• Increased common neighbours increases probability of a relationship

Similarity Utility Calculation

• Indirect Node contacts learnt during a node encounter is represented in and additional matrix

• Node similarity is a simple count of common neighbours

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SimBet Utility Calculation

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•Goal: to select node that represents the best trade off across both attributes

• Combined:

where

SimBet Routing

A B

HELLODeliver msgs

Exchange encounters

Add node encounters

Update betweenness

Update similarityCompare SimBet UtilityExchange Summary Vector

Add node encounters

Update betweenness

Update similarity Exchange messages

Simulation Setup

• Trace based simulation using MIT Reality Mining project data set– An interview obtains an insight over the network

topology– 100 users carrying their cell phones for 10 months

• Comparison– Epidemic Routing [Vahdat and Becker 2000]– PRoPHET [Lindgren, Doria and Schelén 2004]

• Scenario 1: Each node generates a single message for all other nodes

• Scenario 2: Message exchange between least connected nodes

MIT Data set Egocentric Betweenness

Egocentric Betweenness Correlation

Pearson’s Correlation

Egocentric Betweenness

Friendship network Eagle and PentlandEgocentric Betweenness

Delivery Performance

Average End-To-End Delay

Average Number of Hops

Total Number of Forwards

Delivery Performance between least connected nodes

CONCLUSIONS

• Simple metrics for capturing network social structure suitable for disconnected delay-tolerant MANETs– Egocentric Betweenness – Similarity

• Achieves comparable delivery performance compared to Epidemic Routing– But with lower delivery overhead

• Achieves delivery performance between least connected nodes where PROPHET fails

CRITICISMS…

1. Assumption of bi-directional communication of nodes.

2. Large end-to-end delay – spans up to months.

3. No investigation into effects of varying α and β parameters in experiment.

4. Effect of “Mobility Model” in the performance.

5. Similarity is non-zero only near the destinations.

References

[1] Social Network Analysis for Routing in DisconnectedDelay-Tolerant MANETs, Elizabeth Daly and Mads Haahr

[2] VAHDAT, A., AND BECKER, D. Epidemic routing for partially connected ad hoc networks. Technical Report CS-200006, Duke University (2000).

[3] LINDGREN, A., DORIA, A., AND SCHELÉN, O.Probabilistic routing in intermittently connected networks. Lecture Notes in Computer Science 3126 (2004), 239–254.

Questions…

PROPHET Message Delivery

Geodesic Distance

[43] Bouttier, Jérémie; Di Francesco,P. ,Guitter, E. (July 2003). "Geodesic distance in planar graphs".Nuclear Physics B 663(3):535–567