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Transportation-aware Routing in Delay Tolerant Networks (DTNs) Asia Future Internet 2008 Taekyoung...
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Transcript of Transportation-aware Routing in Delay Tolerant Networks (DTNs) Asia Future Internet 2008 Taekyoung...
Transportation-aware Routing in Delay Tolerant Networks (DTNs)
Asia Future Internet 2008
Taekyoung Kwon
Seoul National University
MMLAB
2
IntroductionIntroduction11
outline
Scenario ModelScenario Model22
Our ApproachesOur Approaches33
SummarySummary44
MMLABIntroduction
DTN Delay (or Disruption) Tolerant Networks
Delay? Disruption? Interplanetary networks Sensor networks
Nodes sleep to save power Vehicular networks
Mobile devices get out of other devices’ radio ranges Opportunistic networks
a sender and a receiver make contact at an unscheduled time Underwater networks
MMLABIntroduction
Motivation DTNs may have to be accommodated in future networks
Intermittent connectivity Long or variable delay Asymmetric data rates Heterogeneous links High packet error rates Limited node uptime
MMLABResearch Issues in DTNs
Delay Tolerant Network Architecture Overall redesign
E.g. Bundle Protocol
Routing Protocols Delivery ratio Reducing delay
Congestion control Distributed Caching Multicast/Anycast
MMLAB IP routing may not work
E2e connectivity may not exist at the same time Routing (e.g. MANET) performs poorly in DTN
environments Some assumptions for routing will not work
E.g. BGP leverages TCP
6
Source: Kevin Fall, IRTF DTN RG
MMLABRelated Work (mobility)
Mobility model
DTN
No Mobility Mobility
Routine Random
Predictable Tendency-based
MMLABRelated work (routing)
Some Routing Strategies Epidemic routing
Flooding Spray and wait (S&W)
Limited number of copies of a message
Important Metrics delivery probability delivery latency overhead ratio
MMLABMotivation
Existing routing protocols use only past information like contact history, etc.
DTN Routing can leverage additional information in the future
speed, direction, destination of mobile node, etc.
We want to propose routing protocol using these additional information
MMLABScenario Model
When to use DTN? DTNs can be used for delay tolerant applications
environmental monitoring, some publish/subscribe applications
We assume that each node has location information E.g. GPS, Navigation, localization techniques
MMLABPotential Approaches
Leveraging mobility information Direction of mobile host Speed of mobile host Location of mobile host’s destination Location of message’s destination
Message’s destination can be fixed or mobile
Our approaches Direction-based Destination-based Transportation info-based
MMLAB
12
Our Approach 1
Direction-Based routing protocol Spray & Wait based Number of tokens: n Number of split tokens depends on direction difference
sender’s direction
hand over n/2 tokens
0 °
90 °
-90°
hand over
n*angle/180° tokens
receiver’s direction
hand over
-n*angle/180° tokens
MMLAB
13
Our Approach 2
Destination-Based routing protocol Spray and wait based Number of tokens for handover
n/2*( distance / max diameter )
Maximum diameter
MAP
Sender’s destination
Receiver’s destination
dist
ance
MMLAB
14
Hybrid of approaches 1 and 2
Direction-Distance-Hybrid (DDH)
n/2*Direction(d1)*Distance(d2)*Speed(s) Direction(): function ranged [0,1] Distance(): function ranged [0,1] Speed(): function ranged [0,1] d1: direction difference of two nodes d2: distance difference of two nodes’ destinations s: difference of nodes’ speeds
Direction Destination Handed over tokens
similar close few
similar far medium
different close medium
different far n/2
MMLAB
15
Simulation results (1/2)
Simulator The Opportunistic Network Environment (ONE) simulator http://www.netlab.tkk.fi/~jo/dtn/
Parameter settings
Parameters Value
Area size (m*m) 4500 X 3400
Number of nodes 100 (mobile), 10 (static)
Transmission range (m) 100
Speed (m/s) 0~18
Buffer size (GB) 1 (mobile), 200 (static)
Message size (MB) 0.01 ~ 3
Transmission rate (KB) 250
Movement model Random waypoint
MMLAB
16
Simulation results (2/2) Comparison btw. S&W and DDH
DDH can deliver 18% more packets than S&W When destination is fixed
* : # of delivered packets per 1000 relayed packets
MMLABProblem of Previous Approaches
Randomization effect problem It is caused by local view of tendency As number of contacts is increased, direction or distance is
randomized Effect of our proposal gets reduced
Angle = 90°
∴ handover n/2 tokens Angle = 90°
∴ handover n/4 tokens
An illustration Some tokens can be carried in the
same direction movement information that decides
the number of copies relayed becomes meaningless
2nd contact
1st contact
MMLABScenario Model
A DTN area consists of a certain number of subareas or regions
There is a need of DTN between regions due to poor infrastructure or delay tolerant application
How to dissemination messages between regions efficiently
Region 1Region 1 Region 2Region 2
MMLAB Our Approach 3
Prevention of randomization problem using history Area is divided into several sub areas with non uniform distribution Token handover policy
When a source creates the message, it reserves a fixed number of tokens for each sub-area
If the source meets a mobile host toward other regions, it sends the message to the host with pre-reserved tokens
Tokens can be distributed more evenly across the area
19
MMLAB Simulation Settings
Simulator: Opportunistic Network Environment (ONE)
Area size: 45 X 34km2
4 sub-areas (20x15km2 each) # of nodes: 500
Intra-area node & Inter-area node
Tx range: 100m Speed: 100km/h, 4~60km/h S&W copies: 32 Packet
# of packets: 1000 (2 packets per each node) Packet size: ~ 30KB
Buffer size big enough
MMLAB Simulation Results Destination is mobile Delivery ratio
= # of delivered packets / # of originated packets
Delivery Probability (20% Inter-area Mobile Nodes)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.5 1 1.5 2
DaysDeliver
y Pr
obab
ility
epi_20snw_20our_20
MMLAB Simulation Results
Overhead ratio= (# of relayed - # of delivered) /
# of delivered
Average number of relay nodes
0
50
100
150
200
250
300
350
400
Epidemic SprayAndWait Region- based
Ove
rhea
d R
atio
10% 20%
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Epidemic SprayAndWait Region- based
# o
f re
laye
d no
des
10% 20%
MMLAB Simulation Results
Avg. latency Med. latency
95000
100000
105000
110000
115000
120000
Epidemic SprayAndWait Region- based
Late
ncy
Avg
.
10% 20%
85000
90000
95000
100000
105000
110000
115000
Epidemic SprayAndWait Region- based
Late
ncy
Med
.
10% 20%
MMLABConclusions
DTNs may play a vital role in future Routing is a key player in DTNs We proposed
Direction-based Distance-based Transportation info-based
Destination’s mobility affects the routing performance The more information, the better routing