chapter 1: social-based routing protocols in opportunistic networks
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Routing in Opportunistic Networks. Chapter 1: Social-based Routing Protocols in Opportunistic Networks. Ying Zhu and Yu Wang University of North Carolina at Charlotte. Outline. Introduction Social Properties Social-based Routing Conclusion. Routing in Opportunistic Networks. - PowerPoint PPT PresentationTRANSCRIPT
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 1
Chapter 1: Social-based Routing Protocols in
Opportunistic Networks
Ying Zhu and Yu Wang
University of North Carolina at Charlotte
Routing in Opportunistic Networks
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 2
Outline
Introduction Social Properties Social-based Routing Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 3
Routing in Opportunistic Networks
Intermittent Connectivity in OppNets “Store and Forward“
No connection available?
No connection available? Store & carry
the dataStore & carry
the data
Make forwarding
decision based on certain
routing strategy
Make forwarding
decision based on certain
routing strategy
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 4
Routing in Opportunistic Networks
OppNet Routing Strategies : Based on mobility pattern
Unpredictable mobilityUnpredictable mobility
High overheadHigh overhead
Based on social characteristics Long termLong term
Less volatileLess volatile
Low overheadLow overhead
This chapter focuses on social-based routing
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Outline
Introduction Social Properties Social-based Routing Conclusion
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Social Graph
Social Graph : A global mapping of everybody and how they
are related
Vertices: people
Edges: social ties Different social relationships, i.e. friends, co-workersDifferent social relationships, i.e. friends, co-workers
Intuitive source for many social metrics
Sometime is hard to directly obtain
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Contact Graph
Contact Graph : Recording contacts seen in the past
Vertices: Mobile nodes which are carried by peoplewhich are carried by people
Edges: One or more past meetings
Indicate node’s relationships in OppNets People with close relationships tend to meet more People with close relationships tend to meet more
often, more regular and with longer durationoften, more regular and with longer duration
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Social Properties: Community
Community : A group of interacting users
Devices within same community have higher
chances encounter each other
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Social Properties: Community
Community Detection Methods : Minimum-cut method
Hierarchical clustering
Girvan-Newman algorithm
Modularity maximization
The Louvain method
Clique based method
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Social Properties: Centrality
Centrality : Social importance of its represented node in a
social network
Degree centrality The number of links upon a given nodeThe number of links upon a given node
Betweenness centrality The number of shortest paths passing via given nodeThe number of shortest paths passing via given node
Closeness centrality An inverse of node’s average shortest distance to all An inverse of node’s average shortest distance to all
other nodesother nodes
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Social Properties: Centrality
Degree centrality
a->3, b->4, others->1a->3, b->4, others->1
Betweenness centrality
a->18, b->24. others->0a->18, b->24. others->0
Closeness centrality
a->2/3, b->3/4, c/d/e->6/13,f/g->3/7a->2/3, b->3/4, c/d/e->6/13,f/g->3/7
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 12
Social Properties: Similarity
Similarity : A measurement on degree of separation A simple way to define: Number of common
neighbors between nodes in social/contact graph
Similarity between
a and c is 1c and e is 3
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Social Properties: Friendship
Friendship :
Close personal/contact relationships
In OppNets, friends may have:
Long-lasting contactsLong-lasting contacts
Regular contactsRegular contacts
Common interestsCommon interests
Similar actionsSimilar actions
Different ways to define
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Outline
Introduction Social Properties Social-based Routing Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 15
Label Routing
Label Routing [Hui & Crowcroft, 2007]
Small label for each node (its social group)
Only forward messages to nodes which has
same label with destination or directly to
destination
Requires little information
Easy to implement
Long delay
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SimBet Routing
SimBet Routing [Daly & Haahr, 2007]
SimBet utility, a weighted combination of
betweenness centrality and similarity
Forward message to node with larger SimBet
utility with destination
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SimBet Routing
SimBet uses local centrality & betweenness to reduce overhead
may lead to inaccurate “bridge” identification
Node u will not pass message to node a considers local SimBet utility
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 18
Bubble Rap Forwarding
Bubble Rap Forwarding [Hui, Crowcroft, Yonek, 2008]
globalcentrality:across whole network
local centrality:within local community
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Bubble Rap Forwarding
Bubble-up on global centrality Forward message to the node with Forward message to the node with
higher global centrality higher global centrality Until it reaches a node belongs to Until it reaches a node belongs to
the same local community as destinationthe same local community as destination
Bubble-up on local centrality Use nodes within destination’s community as relays Use nodes within destination’s community as relays Choose the ones with higher local centralityChoose the ones with higher local centrality
When destination only belongs to communities whose members are all with low global centrality, BubbleRap may fail.
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 20
Social-Based Multicasting
Social Based Multicasting [Gao, et al. 2009]
Cumulative contact probability of node i:
N is the total number of nodes in networkN is the total number of nodes in network
T is the total time periodT is the total time period
λλi,ji,j is average contact rate of Possion process for is average contact rate of Possion process for
node pair (i,j)node pair (i,j)
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Social-Based Multicasting
Single-data multicast Destinations are uniformly distributed All nodes need to be contacted within T Select minimal number of relay nodes Using cumulative contact probabilities Considered as unified knapsack problem
Multi-data multicast Relay and destination in
different communities: Forwarding via gateways (G1, G2)
Relay and destination in same community : Same as single-data multicast
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 22
Homophily Based Data Diffusion
Homophily Based Data Diffusion [Zhang & Zhao,
2009]
When contact time too short or buffer is limited,
need consider data propagation orders
Friends usually share more common interests
than strangers (Friendship is user defined)
Diffuses the most similar data of their common
interests to friend first
Diffusing start from the data most different from
their common interests to strangers
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 23
Friendship Based Routing
Friendship Based Routing [Bulut & Szymanski,
2010]
Social pressures metric(SPM) between i and
j:
f(t) denotes the remaining time to the first f(t) denotes the remaining time to the first
encounter of node i and j after time tencounter of node i and j after time t
T denotes the total time periodT denotes the total time period
Describes the average forwarding delay
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 24
Friendship Based Routing
Link quality: An inverse of SPM
Bigger link quality represents closer friendshipBigger link quality represents closer friendship
Construct friendship community based on
link quality
Forward message to node in the same
friendship community with destination
Forward message to node with stronger
friendship to destination than current node
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 25
Social-aware and Stateless Routing
Social-aware and Stateless Routing (Sane) [Mei et al., 2011]
People with similar interests tend to meet more often
Interest profile for node u: K-dimensional vector Iu
Cosine similarity:
If cosine similarity betwween encounted node and
destination is larger than a threshold, forward
message
Stateless & Scalable
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 26
User-Centric Data Disseination
User-Centric Data Disseination [Gao & Cao,
2012]
Interest profile of node i:
Pij : prob. of user i interested in jth keyword
A data item is described by
the importance of ki
Probability of node i interested in data D:
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 27
User-Centric Data Disseination
Centrality value of node i for data dk at t≤Tk:
TTkk: Time constraint of data d: Time constraint of data dkk
NNii: Set of nodes whose information is maintained : Set of nodes whose information is maintained
by iby i
CCijij(T(Tkk-t): Prob. of node i can forward d-t): Prob. of node i can forward dkk to j within T to j within Tkk--
tt
CCii(k)(k)(t): Expected number of interesters i can (t): Expected number of interesters i can
encounter during Tencounter during Tkk-t -t
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 28
User-Centric Data Disseination
Node i is selected as relay for data dk only if:
NNRRkk(t): The number of selected relays for d(t): The number of selected relays for dKK at time at time
tt
NNIIkk(t): The number of interesters will receive d(t): The number of interesters will receive dkk by by
TTk k , estimated at time t, estimated at time t
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 29
Sociability-Based Routing
Sociability Based Routing [Fabbri and Verdone,
2011]
Sociability indicator: Evaluate node’s forwarding abilityEvaluate node’s forwarding ability
The node’s number of encounters with all other The node’s number of encounters with all other
nodes in the network over a period Tnodes in the network over a period T
Nodes which frequently encounter many different Nodes which frequently encounter many different
nodes have high degree of sociabilitynodes have high degree of sociability
Good forwarder: Nodes with high sociability
Forward packet to the most sociable node
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 30
Summary
Social-based routing uses one or multiple social
properties to make forwarding decision
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 31
Outline
Introduction Social Properties Social-based Routing Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 32
Conclusion
Social-based approaches are promising for OppNets
None of these approaches guarantee perfect routing performance
Performance of routing protocol in OppNets depends heavily on mobility model, environment, node density, social structure, and many other facts
Universal routing solution for all Oppnet application scenarios is extremely hard
For particular Oppnet applications, specific routing protocols and mobility/social models are needed
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 33
Future Directions
Are there new social characteristics better than existing ones?
How to combine multiple social properties efficiently?
How to model and extract accurate social characteristics in dynamic OppNets?
How to combine social-based approaches with other type of routing stratigies?
...
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA 34
Thanks for your attention!