chapter 1: social-based routing protocols in opportunistic networks

© 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


Outline

Introduction Social Properties Social-based Routing Conclusion



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



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


Outline



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


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


Social Properties: Community

Community ： A group of interacting users

Devices within same community have higher

chances encounter each other


Social Properties: Community

Community Detection Methods ： Minimum-cut method

Hierarchical clustering

Girvan-Newman algorithm

Modularity maximization

The Louvain method

Clique based method


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


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


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


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


Outline



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


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


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


Bubble Rap Forwarding

Bubble Rap Forwarding [Hui, Crowcroft, Yonek, 2008]

globalcentrality:across whole network

local centrality:within local community


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.


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)


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


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


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


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


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


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:



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



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


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


Summary

Social-based routing uses one or multiple social

properties to make forwarding decision


Outline



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


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?

...


Thanks for your attention!

chapter 1: social-based routing protocols in opportunistic networks

Documents