1

Delay Tolerant Networks

Arezu MoghadamPhD Candidacy Talk12/18/2007

2

Networking expansion

Internet

Internet

B-ISDN

ATM

OSI

Pervasivecomputing

Sensornetswireless DTN

P2Poverlays

CDNPub/sub

1990

2000

Applicationsrule!

1980 1970

3

Interplanetary communication

Ref: [1] Picture: http://www.intel-research.net/berkeley

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ZebraNet (a real life application)

Data

Data

DataStore-and-forward communications

Data

Tracking node with CPU, FLASH, radio and GPS

Sensor Network Attributes ZebraNet Other Sensor Networks

Node mobility Highly mobile Static or moderate mobile

Communication range Miles Meters

Sensing frequency Constant sensing Sporadic sensing

Sensing device power Hundreds of mW Tens of mW

http://www.princeton.edu/~mrm/zebranet.html

First deployment in 2004 in Kenya

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DTN characteristics Internet environment

End-to-end RTT is not large.

Some path exists between endpoints.

E2E reliability using ARQ works well.

Packet-switching is the right abstraction.

DTN characteristics Very large delays. Intermittent and

scheduled links. Different network

architectures. Conversational

protocols fail. No ARQ.

Ref: [2], [3]

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Agenda

Architecture Routing Multicast Implementation Conclusion

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Architectural requirements Asynchronous message delivery. Naming

Tuples (names): ordered pairs (R, L) No ARQ Reliability

At least Hop-by-hop. Type of links

Scheduled vs. non-scheduled. Contact, an opportunity to transfer the data.

Predictable vs. opportunistic.

Ref: [2], [3], [6]

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Reliability End-to-end vs. per-

hop reliability. Custody transfer

Not delete a message until delivery to another custodian.

Head of line blocking. Even “always on”

link is blocked.

S R

A

B

Data

Persistent message storage

Intermittent link

“Always on” link

R provides store-and-forward service

Ref: [4]

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Suggested architectures Sequential heterogeneous

regions interconnected by gateways.

ParaNet Users access more than

one network over one device.

Different paths for signaling and data.

Challenges Routing, transport

protocol, naming, security over multiple paths and etc.

Interplanetary or satellite

Interplanetary or satellite

SensorsSensors

InternetInternetGW

GW

GW

Bus route

Ref: [2]

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Suggested architectures Sequential heterogeneous

regions interconnected by gateways.

ParaNet Users access more than

one network over one device.

Different paths for signaling and data.

Challenges Routing, transport

protocol, naming, security over multiple paths and etc.

DTNStore and forward

DTNStore and forward

Lightweight cellular network for signaling

Data or control information over satellite

Ref: [5]

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Agenda

Architecture Routing Multicast Implementation Conclusion

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Routing Challenges Routing objectives:

Minimize delay Maximize throughput

Per-hop routing vs. source routing. No end-to-end path

MANET’s routing protocols fail. Proactive and reactive

Store-carry-forward Storage constraints

No Topology knowledge Time varying connectivity graph

Ref: [8]

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Routing Models

Flooding based protocols Epidemic [18], Erasure coding [11]

Knowledge based routing Oracle [8], Message Ferrying [15], [16],

Practical routing [9] Probabilistic routing

PROPHET [13], RPLM [12], MaxProp [14], MobySpace [10]

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Flooding based routing Epidemic [18]

Exchanging summary vectors (hash values).

Erasure coding [11] Use r relays wait for

one or rxk relays and wait for k

Message can be decoded if k relays make it to the destination.

>

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Knowledge based routing

))(),(,),(( tdtcvue nn

An oracle which provides topology info. Contacts, buffer

constraints, traffic demands… [8]

Partial topology info. Message ferrying [15],[16]

Using history to predict future topology. Practical routing [9]

S

u

x

w

v

D

Each edge is a contact meaning an opportunity to transfer data.

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Routing with global knowledge

Oracle; source of knowledge about topology How much knowledge to achieve an acceptable delay. Modified Dijkstra with time varying edge costs. Source routing. The more knowledge the better performance. (too obvious!) Not realistic!

MED(Minimum Expected

Delay)

Modified Dijkstra alg with time varying costs based on average edge waiting time.

Contact summery (avg. waiting time until next contact)

>>Ref: [8]

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Routing with partial knowledge Message ferrying

Ferries broadcast their situation.

Ferry route design to minimize drops NP hard reduced to TSP.

Practical routing Instead of contact

schedules uses contact history.

Per-contact routing vs. per-hop routing.

MF: Sparse MANETswith different deployment areas

12

3

4

Ref: [15] , [16]

Scalability: How increasing number of mobilenodes affects number of ferries?

>>

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Routing with partial knowledge Message ferrying

Ferries broadcast their situation.

Ferry route design to minimize drops NP hard reduced to TSP.

Practical routing Instead of contact

schedules uses contact history.

Per-contact routing. Update the graph upon

contact changes.

Practical routing:Source: A dest: D

Per-hop or per-source: A-B-DPer-contact: A-C-D (don’t wait for B)

A

B C

D

8

22

3

A

B C

D

0

2

3

2

Ref: [9] >>

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Probabilistic routing

B C

.5 .5

Estimate delivery likelihood. Initially assign a

delivery probability to each node.

Update upon meeting a node based on some criteria.

Link state routing to disseminate probability tables.

A

B

C

DA C D

.4 .1 .5 B C

.6 .4

A B D

.4 .2 .4

Ref: [10], [12], [13], [14]

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Probabilistic routing

B C

.5 .5

Estimate delivery likelihood. Initially assign a

delivery probability to each node.

Update upon meeting a node based on some criteria.

Link state routing to disseminate probability tables.

A

B

C

DA C D

.4 .4 .2 B C

.6 .4

A B D

.4 .4 .2

Ref: [10], [12], [13], [14]

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Probabilistic routing

MobySpace [10] Closest mobility pattern. How?(Dimension could grow dramatically!?)

PROPHET [13] Delivery predictability

RPLM [12] Routing with persistent link modelingCost window

MaxProp [14] Delivery probabilityCost of using each node as relay

>>

>

>

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Issues of the probabilistic routing.

Covered No a priori knowledge of contacts. Storage constraint and buffer management. Network wide acks to free up buffer space or provide reliable delivery.

Not covered What initial values to start with to converge to reasonable delivery probabilities? What if nodes change their habits. How adaptive? No mathematical proof of efficiency of the routing algorithms.

Packets with hop counts < thresh Sorted by hop count

High rank Low rank

Packets with hop counts > thresh Sorted by delivery likelihood

Packets transmitted from here Packets deleted from here

Ref: [14], [12]

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Mobility model and performance analysis

Node mobility characteristic better performance analysis.

Algorithms developed for specific scenarios. Random with core aided nodes. Community based. Mixture of RWP and ferries.

Ref: [17], [7]

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Performance evaluation

Model objective

Delivery ratio

Delay Message redundancy

Knowledge

Flooding High Low(the least)

High Buffer congestion

Zero

Knowledge based

MF the highest (even higher

than ER)

Moderate Low Provided to the algorithm

Probabilistic Close to ER with tendency in

mobility

Close to ER with tendency in

mobility

Moderate Memory(learning from

past)

Ref: [7], … , [17]

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Agenda

Architecture Routing Multicast Implementation Conclusion

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Multicast requirements and challenges. Disaster recovery, battlefield…

Distribution of news to a group of users; Who is the recipient?

Group membership changes during data transfer. Routing is the most challenging problem. Multicast semantics

Temporal membership: each message contains a membership interval.

Delivery interval as well as membership interval. Current member: receiver should be a member at

delivery time.

Ref: [19]

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Routing models.

S

R2R1

Broadcast-based routing(BBR)

Epidemic routing to all nodes [19]

R1

R2

S

Group-based routing(UBR)

Forwarding group [19]

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Routing models contd…R

R

R R R

F

MFER; MF with Epidemic routing [20]

R

R

R R R

F

MFGR; MF with group routing [20]

S

R1

R2

Tree-based routing(TBR)

Along the spanning treecontaining all receivers [19]

>>

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Performance

Model objective

Delivery ratio

Delay Message redundancy

Topology Knowledge

Flooding High (the best)

Low High Buffer

congestion

Not required

Tree based Moderate High Low Required

MF

ER

GR

close to ER Low High Ferry location

Moderate (large group close to ER)

Moderate Low Ferry location

Ref: [21] , [20] , [19]

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Agenda

Architecture Routing Multicast Implementation Conclusion

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TEK system Searching WWW using

email. Email-based

communication protocol. TEK server located at MIT. TEK client a Java proxy

server. Batched requests are

emailed to the server.

ISP

TEKServer

WebBrowser

TEK Proxy

TEK Client

WWW

WWW

MIT

Remote

Store-and-

forward

Store-and-

forward

Ref: [23] , [25], [22]

Req

Rep

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7DS Based on epidemic

routing. Utilizing opportunistic

contacts to pass email messages.

Basic platform to develop store-and-forward applications.

InternetInternet

Ref: [26]

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Agenda

Architecture Routing Multicast Implementation Conclusion

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Conclusions and future directions A killer application!

Implementation efforts have been limited to specific not everyday life applications.

When Joint tactical radio system becomes available? [25] ParaNet!?

Challenges: topology estimation and routing. So far research focus on predictable network topologies. Knowledge based approaches requiring a global view of

the network are unrealistic. Hybrid of MF with probabilistic routing!? Absence of real world mobility patterns in algorithms

evaluations. Security issues still not discussed! Lack of common APIs to abstract DTN.

35

References

Papers list

36

Back up slides…

37

Probabilistic routing criteria PROPHET

Delivery predictability calculation. Routing with Persistent Link Modeling (RPLM)

Monitors link connectivity to calculate its cost. Dijkstra to find a minimum cost path.

MaxProp Assigning a cost value to each destination based on

probability. Priority queue younger messages higher chances.

MobySpace MobyPoint each node’s coordinates or mobility pattern. Distance on each axes probability of contacts or presence

in a location.

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Routing with global knowledge Message arrival time at a

node must be predicted. Predicted arrival time is

used to determine the cost

At light load ED performance comparable to EDAQ and EDLQ.

Heavy traffic results in congested queues Algorithms with queue knowledge are the winners.

MED Dijkstra with time varying costs based on average edge waiting time.

Contact summery (avg. waiting time until next contact)

ED(Earliest delivery)

Dijkstra’s with time varying costs based on edge waiting time.

Contacts(no knowledge

of queues)

EDLQ(ED with

local queue)

ED with local queuing information.

Contacts + (data queues

for the contact at the current

time)

EDAQ ED with global queuing information.

Contacts + Buffer (queue

sizes across entire topology)

LP Linear programming

All + traffic

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VANETS Propagation of location

specific information. Directional propagation

protocol Custody transfer protocol Inter-cluster routing

protocol Intra-cluster routing

protocol Routing based on local

parameters and TTL Routing in the absence of a

global naming scheme. Ex: traffic data to cars 5

miles away…

H T

HT HT

West

East

40

PROPHET Delivery predictability is calculated at each

node for all destinations B; P(A,B) When node A encounters node B the

parameter P(A,B) is updated.

Packet transfer if delivery predictability at new node is higher than current one.

kold

initoldold

baPbaP

PbaPbaPbaP

),(),(

)),(1(),(),(

41

Link Cost History Idea is cost is related to the

duration of connectivity.

Link with high transitions will get connected soon.

Compared with PROPHET Single forwarding Multi-forwarding

PROPHET doesn’t differentiate between carriers X and Y.

transitonji

N

r

rjiwindowt

ji N

TTC

Transitionji

,

1,_cos

, 1

)(1,

42

Erasure Routing Transforms a message of n

blocks to a message of > n blocks.

Receiver can recover the original message from a subset of blocks

Fraction of the required blocks is the ratio r.

1/r blocks are necessary Instead of propagating among r

relays as in srep distributes them among rk

Whether to use r relays and wait for one to succeed or to use rk relays and wait for k to succeed?

Worst case scenario

43

Practical routing MEED Minimizing estimated expected delay. Using the contact history instead of contact schedule. Nodes record connection and disconnection periods over a

sliding window. Propagating link state table. Per-contact routing instead of source or per-hop routing.

A

B C

D

8

22

3

A

B C

D

0

2

3

2

44

Practical routing simulation

Wireless LAN traces converted into a DTN scenario

Nodes are connected when associated to the same AP

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Message Ferrying – Single Node Initiated MF

The ferry moves according to a specific route Nodes make proactive movement to meet up with ferry Message drops: buffer overflow or message time out Nodes task time vs. meeting the ferry

Ferry Initiated MF Long range radios in nodes. Service_Request Location_Update

Ferry trajectory control based on minimizing message drop rate along the path.

NP-hard problem Nearest Neighbor Traffic aware

)/())()(( fi

nididi GGTtmtD

k

i

s

l

fi

ni

Pi

ltDltDD1 0

00 ))()((

46

Message Ferrying – Multiple To allow scalability

in traffic load Single ferry single

point of failure Different scenarios

No interaction Ferry relaying Node relaying

Designing the ferry routes to minimize weighted delay.

nji ij

nji ijijdD

,1

,1

47

Ferry route design Assigning nodes to ferries to

minimize weighted delay. Optimization problem with

BW constraints The higher the data rate the

longer the route length.

48

Multicasting with MF Long-duration partitions

makes multicast forwarding structure spanning all group members difficult.

Hybrid approach for Ferry initiated MC Message Ferry with

Epidemic Routing Message Ferry with Group

Routing Adaptive Scheme