social network analysis for routing in disconnected delay-tolerant networks
DESCRIPTION
Delay-Tolerant Networks (DTNs) Related Works (Prophet, Epidemic) Motivations Proposed SimBet SimBet Utility Simulation Results ConclusionsTRANSCRIPT
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
Betweenness
Egocentric
Betweenness
w1 3.75 0.83
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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
nnn BetUtilSimUtildSimBetUtil )(
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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