ROUTING IN OPPORTUNISTIC NETWORKS
Chapter 1:
Identifying the Intertwined Links between
Mobility and Routing in Opportunistic
Networks
Xiaoyan Hong
Bo Gu
University of Alabama
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Outline
Introduction
Mobility models
Mobility characteristics
Routing protocols
Future directions
Summary
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MOTIVATION
 Mobility intertwines with routing protocols to play a vital
role in opportunistic networks
 Mobility properties are utilized by routing protocols to
improve performance
 Study on mobility models, analytical results on motion
characteristics and routing strategies will help developing
novel integrated mobility and message dissemination
solutions for opportunistic networks
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INTRODUCTION
 Present a survey over mobility models, analytical results on
motion characteristics and routing strategies
 Mobility models are the evaluation tools for routing
protocols and the sources for movement pattern analysis
 Analytical results contribute to new mobility models with
increased flexibility in reproducing desired network
scenarios
 Routing protocols can make use of underlying mobile
topological structures from results of mobility analysis
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Intertwined Three Components
Motion Characteristics
Spatial properties
Temporal properties
Graph properties
Routing Protocols
Mobility Models
Proactive routing
no map, no intention
w map, no intention
no map, w intention
w map, w intention
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Evaluation
Reactive routing
-contact based
-community based
-auxiliary node based
Outline: Mobility Models
New model
Mobility Models
no map, no intention
Analysis
6
Graph properties
Assist
routing
no map, w intention
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Spatial properties
Temporal properties
w map, no intention
w map, w intention
Motion Characteristics
Routing Protocols
Evaluation
Proactive routing
Reactive routing
-contact based
-community based
-auxiliary node based
MOBILITY MODELS
 Movements are most likely the explicit or implicit results of
their social or personal activities.
 Physical locations
 Social intentions
Classifications

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

Non-Map Without-Intention Models
Map Without-Intention Models
Non-Map With-Intention Models
Map With-Intention Models
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Non-Map Without-Intention Models
 Attributes:
 no restrictions on paths nor intention of movement
 Basic model
 Random Walk Model [8]: Memoryless
 Random Waypoint Model [28]: Delay factor to simulate pauses
 Random Direction Mobility Model[43]: Additionally deal with the
movements when hitting simulation boundary
 Realistic model
 Gauss-Markov Mobility Model [30]: Simulate the acceleration and
deceleration
 Heterogeneous Random Walk[40]: Simulate the clustered network
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Map Without-Intention Models
 Attributes:
 movements are restricted to physical world paths.
 Freeway model[1]: Vertical and horizontal tracks of freeway
 City block[14]: Street grid
 Street Random Waypoint mobility model[11]: Considering
the intra-segment mobility and inter-segment mobility on
street grid
 Vehicular network model[44]: Stop signs, timed traffic
lights and control on next road
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Non-Map With-Intention Models
 Attributes:
 No path restriction
 With individual or shared movement intentions
 Group based model
 Reference Point Group Mobility Model (RPGM)[22]: paths of nodes
in the same group following the movement of the group leader
 Interaction-based mobility model[34]: characterizes the formation
and disaggregation of hot spots at random times and locations
 Community based model
 Community based mobility model[35]: Captures the feature that a
number of hosts are grouped together
 Community model with cyclic pattern[54]: defines the repeating time
period to model re-visits to the same locations
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Map With-Intention Models
 Attributes
 Realistic features such as moving along paths and with intentions
 Trace based model
 Bus traces[2], GPS trace[9], WLAN trace[51], Trace in campus [23]
 Agenda Driven Mobility model[59]: use National Household Travel
Survey (NHTS) data to synthesize each node’s agenda, which derives
its mobility of when, where and what (pause time)
 Graph-based model
 Area Graph based mobility model[4]: A directed and weighted graph
to model locations and paths between locations
 Levy walk based model
 Heavy-tail distribution[41]: movement increment is distributed
according to a heavy-tail distribution
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Summary of the Models
 Trend of mobility modeling has moved towards more
realistic by taking considerations of both social intentions
and geographical features
 Artificially consider social interaction and attraction
 Analyzing real world traces
 WLAN associations give hits on mobility
 Impact
 Effective evaluation tools
 Play an important role for message forwarding in opportunistic
networks
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Outline: Mobility Characteristics
Motion Characteristics
Spatial properties
Temporal properties
Graph properties
Routing Protocols
Mobility Models
Proactive routing
no map, no intention
w map, no intention
no map, w intention
w map, w intention
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Evaluation
Reactive routing
-contact based
-community based
-auxiliary node based
MOBILITY CHARACTERISTICS
Contribute to performance evaluation, simulation
calibration, routings protocol design
Classifications





Characteristics of Flight
Locality Distribution
Temporal Characteristics
Joint Spatial and Temporal Analysis
Graph Characteristics
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Characteristics of Flight
 Flight: the longest straight line trip from one location to
another
 Flight length distribution can be heavy-tail, or exponential
 Flight reflects the diffusivity of mobility
 Models with different diffusivity
 Random Waypoint model, Brownian Motion, Levy Walk model
 Impact
 Diffusive nodes are helpful for relaying messages to larger areas
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Locality Distribution
 Different movement patterns lead to various spatial locality
distributions
 Distributions can be uniform or heterogeneous
 Discussed models:
 Brownian-motion, Random Waypoint Model, Heterogeneous Random
Walk
 Impact
 Cluster based routing is suitable in networks with heterogeneous
distribution
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Temporal Characteristics
 Many properties have been analyzed:
 encounter frequency, pause time, hitting time, meeting time, intercontact time, filling time, scattering time
 Impact
 Encounter history matters for choosing next forwarder
 Pause time, hitting time, meeting time, inter-contact time are useful
in estimating message delay and delivery rate
 Filling time and scattering time describe the dynamics of hot spots,
can be useful for cluster-based routing
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Joint Spatial and Temporal Analysis
 Time and space are closely related in mobility
 Trajectory similarity: Compute similarity using a set of
metrics including Euclidean distance, etc.
 Discussed models: Vehicular model[29], Mobyspace [27],
location based time-dependent link analysis[20][21]
 Impact
 Routing uses clusters or high similarity nodes
 Help to identify popular locations in mobile networks and trajectory
segments
 Calculate communication latency
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Graph Characteristics
 Using graph properties to identify mobility patterns
 Centrality [17]
 Degree centrality, closeness centrality, betweenness centrality
 Social networks
 k-clique community, network connectivity
 Discussed models: Clique community[25], Continuum
framework [10]
 Impact
 Node with higher centrality as forwarder, community helps to group
mobile nodes, connectivity analysis
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Summary: Characteristic Analysis (I)
Categories
Mobility Characteristics
Features for Routing
Flight Length
Longest straight line trip
from one location
to next location; node
diffusivity
Message forwarder
adopts high
diffusive nodes for fast
dissemination
Locality
Distribution
Distribution of node
positions during moving
process is either uniform
or heterogeneous
Cluster based routing is
suitable in networks
with heterogeneous
distribution
Temporal
Characteristics
Encounter frequency,
pause time, hitting
time, meeting time, intercontact time, filling
time, scattering time
Encounter history for
choosing next forwarder;
Estimating
message delay and
delivery rate
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Summary: Characteristic Analysis(II)
Categories
Mobility Characteristics
Features for Routing
Joint SpatialTemporal
Time and location
relationships of groups,
trajectory similarity
Routing uses clusters or
nodes with high similarity
Graph
Characteristics
Degree centrality,
closeness centrality,
betweenness centrality,
k-clique community
Nodes with higher
centrality as forwarder;
community helps to group
mobile nodes;
connectivity analysis and
evolution for performance
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Outline: Routing Protocols
Motion Characteristics
Spatial properties
Temporal properties
Analysis
New model
Graph properties
Mobility Models
no map, no intention
w map, no intention
no map, w intention
w map, w intention
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Routing Protocols
Proactive routing
Reactive routing
-contact based
-community based
-auxiliary node
based
ROUTING STRATEGIES
 Routing principle: store-carry-forward
 Classifications:
 Proactive Routing: with centralized or off-line knowledge about
network
 Reactive Routing: without a global or predetermined knowledge
• Contact based routing: forward messages using the encounter history
• Community based routing: identify and rely on various clusters
• Auxiliary node based routing: introduce mobile or static message ferries
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Proactive Routing
 knowledge such as contacts history, queuing length and
traffic demands
 Use a graph with time-varying delay and capacity
 Discussed protocols:
 Framework of DTN routing which takes different levels of network
knowledge [26]
 Treat routing as a resource allocation problem[2]
 Link with contact probability calculated from cyclic movement
pattern [32]
 Routing assisted by static relay nodes deployed at critical locations
for cyclic movement pattern[19]
 Mobyspace with the assumption of full network knowledge [27]
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Reactive: Contact based routing
 Forwarding decision is made when two nodes encounter each
other
 Discussed protocols
 Epidemic routing [52]: forward to each contact
 PROPHET: employ a probabilistic metric called delivery predictability
[31]
 Spray and Wait protocol: broadcasts only a fixed number of copies
of message [49]
 Seek and Focus protocol: hybrid protocol which includes utilitybased routing and randomized routing [49]
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Reactive: Community based routing
 Identify and use a special group of nodes
 Better sociability
 Frequent contacts with the destinations
 Attached to a hot location
 Discussed Protocols




Distributed method to identify central nodes[13]
Multicast routing [18]
Island Hopping [46]
Connected dominating set for VANET [33]
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Reactive: Auxiliary node based routing
 Introduce nodes specially designed for message relay,
either mobile or static
 Discussed routing




Auxiliary node with Levy Walk pattern[47]
Levy Walk searching[53]
Mobile message ferry[57]
Static throw box[58]
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Summary: Routing Protocols
 Relationships among routing strategies, mobility models and
their characteristics
 TABLE II and TABLE III summarize the following





Categories
Routing protocols
Main routing strategies
Mobility models and features
Applicable environments
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FUTURE DIRECTIONS
 Social network related analysis and its connection to
opportunistic networks
 Movements within a real road system
 Novel message dissemination schemes that explore new
social network properties
 Management of opportunistic networks, examples include
extending coverage, capacity and traffic aggregation
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CHAPTER SUMMARY
This chapter presents a survey over mobility
models, analytical results on motion
characteristics, and routing strategies that
largely rely on mobility in opportunistic networks
More important, it provides a systematical overview
and identifies the intertwining connections among
the three areas.
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CHAPTER SUMMARY
Applications of
Opportunistic Networks
Comm. support
Routing Schemes
Mobility assistance
Evaluation
Mobility Characteristics
Abstraction
Movement Patterns
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Gossipmule, content spreading in mobile
social networks, opportunistic Internet
access, rural area networks)
Proactive routing,
reactive routing (contact based,
community based, auxiliary node based)))
Flight, locality, temporal characteristics,
joint spatial-temporal, graph features
Random walk,
Random waypoint,…
Group Mobility
model, community
based model,…
Manhattan Model,
Freeway model, …
Trace based
model, Graph
based model,…
Thanks for your
attention!
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