Routing in Opportunistic Networks
Chapter 1:
Social-based Routing Protocols in
Opportunistic Networks
Ying Zhu and Yu Wang
University of North Carolina at Charlotte
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
1
Outline




Introduction
Social Properties
Social-based Routing
Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
2
Routing in Opportunistic Networks
 Intermittent Connectivity in OppNets
 “Store and Forward“
No connection
available？
Store & carry
the data
Make forwarding
decision based on
certain routing
strategy
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
3
Routing in Opportunistic Networks
 OppNet Routing Strategies：
 Based on mobility pattern
 Unpredictable mobility
 High overhead
 Based on social characteristics
 Long term
 Less volatile
 Low overhead
This chapter focuses on social-based routing
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
4
Outline




Introduction
Social Properties
Social-based Routing
Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
5
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-workers
 Intuitive source for many social metrics
 Sometime is hard to directly obtain
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
6
Contact Graph
 Contact Graph：
 Recording contacts seen in the past
 Vertices: Mobile nodes
 which are carried by people
 Edges: One or more past meetings
 Indicate node’s relationships in OppNets
 People with close relationships tend to meet more often,
more regular and with longer duration
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
7
Social Properties: Community
 Community：
 A group of interacting users
 Devices within same community have higher
chances encounter each other
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
8
Social Properties: Community
 Community Detection Methods：
 Minimum-cut method
 Hierarchical clustering
 Girvan-Newman algorithm
 Modularity maximization
 The Louvain method
 Clique based method
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
9
Social Properties: Centrality
 Centrality：
 Social importance of its represented node in a
social network
 Degree centrality
 The number of links upon a given node
 Betweenness centrality
 The number of shortest paths passing via given node
 Closeness centrality
 An inverse of node’s average shortest distance to all
other nodes
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
10
Social Properties: Centrality
 Degree centrality
a->3, b->4, others->1
 Betweenness centrality
a->18, b->24. others->0
 Closeness centrality
a->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
11
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 1
c and e is 3
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
12
Social Properties: Friendship
 Friendship：
 Close personal/contact relationships
 In OppNets, friends may have:
 Long-lasting contacts
 Regular contacts
 Common interests
 Similar actions
 Different ways to define
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
13
Outline




Introduction
Social Properties
Social-based Routing
Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
14
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
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
15
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
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
16
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
17
Bubble Rap Forwarding
 Bubble Rap Forwarding
[Hui, Crowcroft, Yonek, 2008]
global
centrality:
across whole
network
local
centrality:
within local
community
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
18
Bubble Rap Forwarding
 Bubble-up on global centrality
 Forward message to the node with
higher global centrality
 Until it reaches a node belongs to
the same local community as destination
 Bubble-up on local centrality
 Use nodes within destination’s community as relays
 Choose 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
19
Social-Based Multicasting
Social Based Multicasting
[Gao, et al. 2009]
 Cumulative contact probability of node i:
 N is the total number of nodes in network
 T is the total time period
 λi,j is average contact rate of Possion process for node
pair (i,j)
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
20
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
21
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
22
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 encounter
of node i and j after time t
 T denotes the total time period
 Describes the average forwarding delay
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
23
Friendship Based Routing
 Link quality: An inverse of SPM
 Bigger 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
24
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
25
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
26
User-Centric Data Disseination
 Centrality value of node i for data dk at t≤Tk:
 Tk: Time constraint of data dk
 Ni: Set of nodes whose information is maintained by i
 Cij(Tk-t): Prob. of node i can forward dk to j within Tk-t
 Ci(k)(t): Expected number of interesters i can
encounter during Tk-t
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
27
User-Centric Data Disseination
 Node i is selected as relay for data dk only if:
 NRk(t): The number of selected relays for dK at time t
 NIk(t): The number of interesters will receive dk by Tk ,
estimated at time t
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
28
Sociability-Based Routing
Sociability Based Routing
[Fabbri and Verdone, 2011]
 Sociability indicator:
 Evaluate node’s forwarding ability
 The node’s number of encounters with all other nodes
in the network over a period T
 Nodes which frequently encounter many different
nodes 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
29
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
30
Outline




Introduction
Social Properties
Social-based Routing
Conclusion
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
31
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
32
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
33
Thanks for your
attention!
© Y. Zhu and Y. Wang @ University of North Carolina at Charlotte, USA
34