Xiaoyan Hong Bo Gu University of Alabama

© University of Alabama 1

Chapter 1: Identifying the Intertwined Links between Mobility and Routing in

Opportunistic NetworksXiaoyan Hong

Bo Gu

University of Alabama

ROUTING IN OPPORTUNISTIC NETWORKS


OutlineIntroductionMobility modelsMobility characteristicsRouting protocolsFuture directionsSummary


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


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


Intertwined Three Components

Spatial propertiesTemporal properties

Graph properties

Motion Characteristics

Proactive routingReactive routing -contact based -community based-auxiliary node based

Routing Protocols

no map, no intentionw map, no

intentionno map, w intention

Mobility Models

w map, w intention

New m

odel

Analys

is

Assist routing

Evaluation


Outline: Mobility Models

Spatial properties

Temporal propertiesGraph properties



Routing Protocols

no map, no intention

w map, no intentionno map, w intention

Mobility Models

w map, w intention

New model

Analysis

Assist routing

Evaluation


MOBILITY MODELS Movements are most likely the explicit or implicit

results of their social or personal activities. Physical locations Social intentions

Classifications Non-Map Without-Intention Models Map Without-Intention Models Non-Map With-Intention Models Map With-Intention Models


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


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


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


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


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


Outline: Mobility Characteristics

Spatial properties

Graph properties



Routing Protocols



Mobility Models

w map, w intention

New m

odel

Anal

ysis

Assist routing

Evaluation

Temporal properties


MOBILITY CHARACTERISTICSContribute to performance evaluation,

simulation calibration, routings protocol design

Classifications Characteristics of Flight Locality Distribution Temporal Characteristics Joint Spatial and Temporal Analysis Graph Characteristics


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


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


Temporal Characteristics Many properties have been analyzed:

encounter frequency, pause time, hitting time, meeting time, inter-contact 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


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


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


Summary: Characteristic Analysis (I)Categories Mobility

CharacteristicsFeatures for Routing

Flight Length Longest straight line trip from one locationto next location; node diffusivity

Message forwarder adopts highdiffusive nodes for fast dissemination

Locality Distribution

Distribution of node positions during movingprocess is either uniform or heterogeneous

Cluster based routing is suitable in networkswith heterogeneous distribution

Temporal Characteristics

Encounter frequency, pause time, hittingtime, meeting time, inter-contact time, fillingtime, scattering time

Encounter history for choosing next forwarder; Estimatingmessage delay and delivery rate


Summary: Characteristic Analysis(II)Categories Mobility

CharacteristicsFeatures for Routing

Joint Spatial-Temporal

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


Outline: Routing Protocols

Spatial propertiesTemporal properties

Graph properties



Routing Protocols



Mobility Models

w map, w intention

New

m

odel

Anal

ysis

Assist routing

Evaluation


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


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]


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 utility-

based routing and randomized routing [49]


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]


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]


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


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


CHAPTER SUMMARYThis 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.


Mobility Characteristics

Movement Patterns

CHAPTER SUMMARY

Random walk,Random

waypoint,…

Manhattan Model,

Freeway model, …Group Mobility

model, community

based model,…

Trace based model, Graph

based model,…

Flight, locality, temporal characteristics,

joint spatial-temporal, graph features

Routing Schemes Proactive routing, reactive routing (contact based, community based, auxiliary node

based)))

Applications of Opportunistic Networks

Gossipmule, content spreading in mobile social networks,

opportunistic Internet access, rural area networks)

Abstraction

Mobility assistance Evaluation

Comm. support


Thanks for your attention!

Documents

Xiaoyan Hong Bo Gu University of Alabama