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Routing in Disruption-Tolerant Networks

Katia ObraczkaUniversity of California, Santa Cruz

katia@soe.ucsc.edu

http://inrg.cse.ucsc.edu/

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What are Disruption-Tolerant Networks?

• Disruption-tolerant networks or DTNs.

• A.k.a, – Delay-tolerant, – Episodically-connected,– Intermittently-connected networks.

Networks where end-to-end connectivity is NOT guaranteed.

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DTN Applications: Military Operations

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DTN Applications: Environmental Monitoring

CARNIVORES Project at UCSC: Monitoringcoyotes in the SantaCruz mountains.

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DTN Applications: Connecting Remote Communities

• Rural “kiosks”:– Shared among locals.– Selling/buying

agricultural products.– Banking and other

transactions.

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S

DX Xpath

disruption!

DTNs

Connectivity disruption due to:• Wireless propagation (fading, shadowing, etc.)• Duty cycling for power conservation.• Mobility.

Regular Network DTN

S D

End-to-end path

S

DX

Xpath disruption!

nodelink

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What’s the big deal?• Routing protocols have always assumed

end-to-end connectivity.– Table-driven (proactive) protocols (e.g., Internet

routing) can recover from infrequent topology changes.

– On-demand (reactive) protocols (e.g., MANET routing) can recover from frequent but short-lived outages.

• But what if “outages” are frequent and long-lived?– “Traditional” routing simply drops packets!

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DTN’s Routing Paradigm Shift

• Before DTNs:– Space dependency.– Network routing:

given graph G(V,E), find shortest path between source-destination.

– Store-and-forward routing.

• After DTNs:– Space and time

dependency.– Network as a time-

varying graph G(V,E(t)).

– Links are a function of time.

– Links as “contacts”.– Store-carry-and-

forward routing.

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Types of Contacts• Scheduled contacts

– E.g. satellite links, message ferry.

– All info known.

• Probabilistic contacts– Statistics about

contacts known.– E.g., bus, sensors with

random wake-up schedule.

• Opportunistic contacts– Not known a priori.– E.g., tourist car that

happens to drive by.

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Designing DTN Routing/Forwarding Protocols

• What information is available?– Oracles.– Contacts, contact statistics, queuing, traffic, buffer

capacity, etc.– None.

• How much information is known?– No knowledge.– Partial knowledge.– Complete knowledge.

• Trade-offs?

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Routing/Forwarding under Intermittent Connectivity

Scheduled/, (partially) known contacts (e.g., buses).

Enforced contacts with specialized nodes (e.g., ferries).

What about unknown contacts?– Contacts not known in advance.– No specialized nodes. i.e., only mobility of the nodes

themselves is available.

Opportunistic (mobility-assisted) routing

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Our Focus: Opportunistic Routing

Generality and simplicity: no knowledge assumed.

• Opportunistic routing paradigm:– At every hop, node decides whether to:

• Forward and/or• Store-and-carry.

– Store-carry-and-forward.

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Current Research

• Two thrusts:– Utility-based controlled replication in

heterogeneous DTNs.

– Steward-Assisted Routing (StAR)• Routing framework that works well in both

connected networks and networks prone to frequent, long-lived disconnections.

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Utility-Based Replication in Heterogeneous DTNs

• In collaboration with Akis Spyropoulos and Thierry Turletti, INRIA Sophia-Antipolis.

T. Spyropoulos, T. Turletti, and K. Obraczka, ``Utility-based Message Replication for Intermittently Connected Heterogeneous Networks'', in the Proceedings of the 1st. IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC) 2007.

T. Spyropoulos, T. Turletti, and K. Obraczka "Routing in Delay-Tolerant Networks Comprising Heterogeneous Node Populations”, in preparation.

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Opportunistic Routing• Epidemic Routing: everyone gets a copy (Vahdat et al.

‘00).

(+) Fast (for low traffic loads!). (-) Too many resources!

• Controlled Replication: give a copy to only L nodes (Small et al. ’05, Spyropoulos et al. ’05).

(+) Control transmissions/energy/buffer space per message!

(+) few copies (<10% of nodes) give good performance (not always!)

(-) Replication is greedy: blindly choose relays!

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Greedy Replication: A Good Scenario

Covered by Relay 1

3

S

D

1

2

4

5

6

7

8

9

10

11

12 13

14

1615

Covered by relay 2

2 copies

Relays are highly mobile

Relays routes are uncorrelated

Nodes have homogeneous behavior

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Heterogeneous Networks

Community (local) Nodes

Base Stations or Static Sensors

Fast/Mobile Nodes

stay inside community

Roam around network(infrequent)

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Greedy Replication in Heterogeneous Environments

• If p% of the nodes are “useless” (local):– Delay increase ≈ c1 + c2/(1-p) (c1, c2

constants, and c1 + c2 = 1) compared to homogeneous case.

Src

Dst

3 copies

Move “locally”

Roaming NodeGood Relay!No more copies!

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Goal: Discover “Best” RelaysHow? Maintain some utility function.

– What does “better” mean?

• 2 types of utility function: Destination-Dependent (DD) Utility:

“Node X is a good next hop for destination D”.– E.g., friendship with D, proximity to D, etc. Destination-Independent (DI) Utility:

“Node X is a good next hop for all destinations”.– E.g., moves more frequently/faster around the network,

is a “social hub”, has higher resources (e.g. vehicle with no energy limitations), etc.

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What to Do With Utility?Approach: Give L copies to best relays:

(+) Control resources: IMPORTANT! (e.g., scarce resources like battery power)

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Utility-based (“Smart”) Replication

• Message from S to D.

• S starts with 1 message copy and L “fwd tokens”.

• UX(Y): Utility of node X for destination Y.

SD

D

Option 1) If UB(D) > UA(D) (relative utility)

Option 2) If UB(D) > Uth (absolute utility)

L tokens

A

D

n>1 tokens

B

D

n/2n/2

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Last Seen First (LSF) Replication

• Age-of-last-encounter timer: “I last saw node D less than 10 minutes ago”.

• tX(Y): last time X saw Y.

• UX(Y) = 1/(tX(Y)+1)– If my timer is smaller, then I’m a better relay

for that destination.– DD utility.

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LSF Spraying

0

0.5

1

1.5

2

2.5

3

3.5

4

L=10, K=40 L=16, K=40 L=10, K=50 L=16, K=50

Number of copies, Tx Range

Del

ay (

SW

) / D

elay

(L

SF

) LSF (transitivity)

LSF (no transitivity)

Simulation Results (LSF)

Community-based Mobility (Infocom’07)Four types of nodes (100 total)

Community nodes (40%), Local nodes (40%) Roaming nodes (10%), Static nodes (10%)

more copies => less costly mistakes

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Most Mobile First (MMF) Replication

• Utility of X = Label of node X.• DI.

• Preference Order (): Label1 Label2… LabelN.

– E.g. based on statistical properties or characteristics. – Order may also depend on destination’s label.

Community

Base Station

Pedestrian

Pedestrian

VehicleVehicle

Sensor

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Simulation Results – MMF (1)

• Labels: “roam” “community” ”local” “static”• MMF1: give only to {“roam” || “community”}• MMF2: {“roam” || “community”} && {Prob{roam} > 0.15}

Same scenario as before

4 types of nodes

MMF spraying (Scenario 1)

0

0.5

1

1.5

2

2.5

3

3.5

MMF1(L=10) MMF2(L=10) MMF1(L=16) MMF2(L=16)

Protocol (# of copies)

Del

ay (

SW

) / D

elay

(M

MF

) sparse (8% connectivity)

almost connected (52% connectivity)

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Simulation Results – MMF (2)• 2 types of nodes:

“mobile” and “static”

• Algorithm: give only to “mobile”

• Nodes = 100

MMF Spraying (Scenario 2)

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0.4 0.3 0.2 0.1 0.05percentage of mobile nodes (p)

De

lay

(S

W)

/ D

ela

y (

MM

F) K = 40, L = 16

K = 50, L = 10K = 50, L = 16K = 60, L = 16

many “good” options => fewer mistakes by “greedy”

few “good” options => mistakes cost!

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Most Social First (MSF) Algorithm

• Mobility statistics not always available or dynamic (e.g. node in car, then in office)

• Estimate node “sociability” online– tn = [(n-1)T, nT] (nth time window – duration T)

– Ni(n): set of nodes seen by node i during tn

– Si(n) = |Ni(n)|/T: sociability of i during tn

– Running average:

– Utility of node i: Ui =

(n)aSSa)(1S iii ˆˆ

iS

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Tuning MSF’s Parameters• Can past predict future?

• How do we set T (window) and a (weight of new sample)?

– Depends on (i) mobility/interaction patterns, (ii) horizon of prediction

– time-homogeneous: past reliable predictor!– periodic in T*: set T to T*

– shorter scale prediction: smaller T, larger a, higher moments

• Number of copies: find lower bound given target delivery ratio and message TTL.

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Simulation Results – MSF

2 types of nodes: “mobile”, “static” MMF: knows “mobile” labels beforehand MSF: identifies “mobile” nodes online. Same performance!

T = 1000, a = 0.8, L = 10

MSF Spraying (Delivery Ratio)

00.10.20.30.40.50.60.70.80.9

1

0.1 0.2 0.3

Ratio of "useful" nodes (p)

de

live

ry p

rob

ab

ility

MSF Spraying (Delivery Delay)

0

1000

2000

3000

4000

5000

6000

7000

0.1 0.2 0.3Ratio of "useful" nodes (p)

de

liv. d

ela

y(t

ime

un

its

)

Greedy

MMF

MSF

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ConclusionUtility-based vs. Greedy Replication in

Heterogeneous Networks: up to 4-5x improvement– Few “good” options => bigger gains.– Small budget of copies => bigger gains.

MSF: Generic, adaptive algorithm to discover “social” nodes.

Can be a building block for more complex schemes.

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Ongoing and Future Directions

• Modeling encounter-based (epidemic) protocols in heterogeneous environments.– Multiple classes of nodes. – Different mixing characteristics.– Fluid model approximation.

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Ongoing and Future Directions (Cont’d)

• Trace-based evaluation of Smart Replication. – E.g., National University of Singapore class

schedules.

• Hybrid DI/DD utility functions; more sophisticated sociability estimators.

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Steward-Assisted Routing (StAR)

• Routing framework that performs well in both connected networks as well as networks prone to frequent, long-lived disconnections.

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StAR

• In collaboration with Jay Boice (UCSC MSc, May 2007) and J.J. Garcia-Luna.

• J. Boice, J.J. Garcia-Luna Aceves, K. Obraczka, ``Disruption-Tolerant Routing with Scoped Propagation of Control Information'', in Proceedings of the IEEE International Conference on Communications (ICC) 2007.

• J. Boice, J.J. Garcia-Luna Aceves, and K. Obraczka, ``On-Demand Routing in Disrupted Environments'', BEST PAPER AWARD, in Proceedings of the IFIP/TC6 Networking 2007.

• J.J Garcia-Luna Aceves, K. Obraczka, and J. Boice, “An On-Demand Routing Framework for Disruption-Tolerant Networks”, under submission.

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StAR Highlights

• Combines on-demand (intra-partition) with opportunistic (inter-partition) routing.– Use of relays, or stewards, to deliver data to

partitioned destinations.

• No a-priori topological knowledge.– Use past connectivity information to predict future

communication opportunities.

• Scopes temporal and spatial dissemination of routing information.

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SCIP

• Scoped Contact and Interest Propagation.

• Limits scope of routing information.– Nodes get routing info

for destinations of interest.

– Nodes only keep info for d if they are on the path from s to d.

Example: s1 and s2 interested in d.

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Steward Selection

• Steward selected for given destination.

• Use sequence numbers and number of hops to select local steward for destination d.– Steward has most

recent sequence number.

– If sequence numbers are equal, choose node with lowest number of hops to destination.

Example: one steward per destinationper partition.

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Well-Connected Topologies

• 100 nodes in 3600x500m area with full connectivity.

• Static and random waypoint mobility.• Comparison against AODV and OLSR.

StAR performs well with full connectivity and under short-lived disconnections.

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As Connectivity Decreases…

Decreasing connectivity

PDR

AODV

Epidemic

StAR

Gridded mobility.

Decrease connectivity byincreasing grid dimension.

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StAR Experiments: Special Operation Scenarios

• Real experiments with StAR on testbed with static nodes and mobile robots.

• Collaboration with Prof. Weitzenfeld’s robotics lab at ITAM, Mexico.

K. Obraczka, J. Boice, L. Martínez-Gómez, J. P. Francois, A. Levin-Pick, A. Weitzenfeld, “StAR: Ad-Hoc Wireless Networking for Autonomous Multi-Robot Coordination”, to appear in the Proceedings of the 1st. IEEE/ACM Robocom, October 2007.

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StAR Testbed

Eagle Knights modified robot with local camera and 802.11 communicationcapabilities.

The original robot architecture is extended with: (1) the Crossbow Stargatemanaging wireless communication and local vision and (2) a webcam for sensing.

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Results

Topology 2: Static sensors with mobile intermediate node. Sensor 4 sends images to sink node 7 through intermediate mobile node 2 and static node 1.

TABLE IIPerformance of AODV and StAR in Topoloy 2

Image Deliveries DeliveryRatioAODV 48 51.11%StAR 90 100.00%

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Summary

• StAR as routing framework that operates well in both well-connected networks as well as networks prone to episodic connectivity.

• No a-priori knowledge, e.g., node schedules, location, etc.

• Combines on-demand (intra-partition) with opportunistic (inter-partition) routing.– Use of relays, or stewards, to deliver data to

partitioned destinations.

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Future Work

• Use other sources of information to improve performance.– Full/partial node schedules, GPS, etc.

• Investigate other metrics for steward selection.

• Explore different message replication strategies.

• Integrate with work on utility-based opportunistic routing.

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Summary of Other Activities

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Energy-Efficient Medium Access (Task 4)

• Novel efficient and flexible medium access framework form MANETs.

• With J.J. Garcia-Luna and Venkatesh Rajendran (UCSC PHD, May 2007).

V. Rajendran, K. Obraczka and J.J. Garcia-Luna, ``A DYNAmic Multi-channel Medium Access Framework for Wireless Ad Hoc Networks'', BEST PAPER AWARD in the Proceedings of the 4th. IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) 2007.

V. Rajendran, Medium Access Control Protocols for MANETs, PhD Dissertation, UCSC, 2007.

V. Rajendran, K. Obraczka and J.J. Garcia-Luna, Application-Aware Medium Access for Sensor Networks, 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), November 2005.

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Energy Consumption Modeling and Prediction (Task 4)

• Models for energy consumption and network lifetime prediction.

• Collaboration with Prof. R. Manduchi, UCSC CE.

C. Margi, Energy Consumption Trade-Offs in Power-Constrained Networks, PhD Dissertation, UCSC, 2006.

C. Margi, K. Obraczka, R. Manduchi, Characterizing System Level Energy Consumption in Mobile Computing Platforms, IEEE WirelessCom 2005, June 13-16, 2005

C. Margi, V. Petkov, K. Obraczka, R. Manduchi Characterizing Energy Consumption in a Visual Sensor Network Testbed, IEEE/Create-Net TridentCom 2006, March 1-3, 2006.

Energy Consumption Trade-offs in Visual Sensor Networks”. C. B. Margi, R. Manduchi , K. Obraczka. SBRC 2006, May 29 - June 02, 2006.

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Mobility Models for Wireless Networks (Task 1)

• First Statistical-Equivalent Model (SEM) to characterize random waypoint mobility.

• Collaboration with Profs. B. Sanso and A. Kottas, UCSC Applied Math, and K. Viswanath (NTT Labs).

K. Viswanath, A. Kottas, B. Sanso, and K. Obraczka, ``Statistical Equivalent Models for Computer Simulators'', in press to appear in the special issue of Simulation: Transactions of the Society for Modeling and Simulation International on ''Advances in Performance Evaluation of Computer and Telecommunication Systems”.

K. Viswanath, K. Obraczka, A. Kottas, B. Sanso, A Statistical Equivalent Model for Random Waypoint Mobility: A Case Study, IEEE SMC SPECTS 2006.

K. Viswanath and K. Obraczka, Modeling the Performance of Flooding in MANETs (Extended Version), Computer Communications Journal (CCJ) 2005.

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Robust Routing for Network Fault-Tolerance and Security (Task 6)

• Novel game-theoretic stochastic routing framework as proactive alternative to today's reactive approaches to route repair.

• Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA.

S. Bohacek, J.P. Hespanha, C. Lim, and K. Obraczka, ``Game Theoretic Stochastic Routing for Fault Tolerance on Computer Networks'', in press to appear in the IEEE Transactions Parallel and Distributed Systems.

C. Lim, Scalable Multi-path Routing for Robust Communication, PhD Dissertation, USC, 2006.

G. Huang, Robust and Secure Routing in MANETs, MSc Theis, UCSC, 2006. C. Lim, S. Bohacek, J. Hespanha and K. Obraczka, Hierarchical Max-Flow

Routing, IEEE Globecom 2005. S. Bohacek, J. Hespanha, J. Lee, C. Lim and K. Obraczka, A New TCP for

Persistent Packet Reordering, IEEE/ACM Transactions on Networking, Vol. 14, No.2, April 2006.

R. Guru, G. Huang and K. Obraczka, An Integrated and Flexible Approach to Robust and Secure Routing in MANETs, IEEE IC3N, August 2005.