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Chapter 8: Probabilistic Routing Schemes

for Ad-Hoc Opportunistic Networks1Vangelis Angelakis, 2Elias Tragos , 3George Perantinos, and 1Di Yuan

1 Linköping University, Sweden2 Foundation for Research and Technology –Hellas

3 Forthnet S.A.

Routing in Opportunistic Networks

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Wireless proliferation Wireless RF Proliferation in the past decades

Bluetooth, 802.11a,b/g, 3/4G

Computing paradigms based on Wireless Wireless Cloud Internet of Things Machine-to-Machine (ad-hoc) communication

Wireless medium backlashes Range issues Interference / Communication reliability

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Relaying and forwarding Transmission range limitations -> need for relays

Key decisions in forwarding packets: 1. What to send (my packet or a relayed packet ?)2.To whom (to a relay or the destination ?)3.When to do so ( will I suffer collisions, cause interference ?)

Routing deals with 1,2Scheduling takes care of 3 once 1 and 2 have been decided

Relaying typically assumes:Some topology knowledgeCollaborating nodes (limited/no selfishness)

Routing needs to work towards these assumptions

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Routing in Opportunistic Networks The role of mobility

1. Buffering taking advantage of transitive transmission2. Delay\Disruption -Tolerant Networking

Problems arising from opportunistic communication:1.Topology is becoming too variable2.Selfishness can arise to conserve resources

Opportunistic Networks’ routing needs to cope with these two

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Probabilistic Routing Work-around: Probabilistic routing

Model and take into account the environment (too complex), or

Randomize on• Whom to send to andWhom to send to and• When to sendWhen to send

Cross-layer routing approach, taking input from: Physical layer Access layer

Trade-off: performance / simplicity-effectivness

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Probabilistic Routing Work-around: Probabilistic routing

Model and take into account the environment (too complex), or

Randomize on• Whom to send to andWhom to send to and• When to sendWhen to send

Cross-layer routing approach, taking input from: Physical layer Access layer

Trade-off: performance / simplicity-effectivness

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Schemes Overview

1. Epidemic routing (Vahdat & Becker, 2000)2. PROPHET (Lindgren, et al. 2003)3. MAXPROP (Burgess, et al. 2006)4. Parametric Probabilistic Routing (Barret, et al.

2005)5. PROPICMAN (Nguyen, et al. 2007)

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Epidemic Routing 1/2 Bio-inspired: packets are considered to infect nodes

(Vahdat & Becker, 2000)

Assumes Nodes are randomly mobile & have ordered identifiers Resources sufficiency (battery / buffers)

Forwarding Decision: fixed – flooding

Buffers: FIFO

Buffer (hashed) “index”: Summary Vector (SV)

Reliability: ack’s

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Epidemic Routing 2/2 Meeting a newly identified neighbor node

Exchange SVs Exchange unknown messages For protocol sake the process is initiated by the node with the

smaller identifier

Per-host queuing New messages given preference over old ones in

terms of buffer availability

1

BA3

2

Request: (SVA+SVB’)

SVA

Messages unknown to B

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PRoPHET (1/2) PRoPHET: Probabilistic Routing Protocol

using History of Encounters and Transitivity(Lindgren, et al. 2003)

Users move in a “not so random”, predictable fashion

Forwarding decision: by Delivery Predictability P(M,D) set up at every node M for each known destination D.

Epidemic Routing SV’s are used here too to exchange Delivery Predictability values to updated own P(M,D) as

follows:

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PRoPHET (2/2) When the node M encounters another node N, the

predictability for N  increases as:P(M, N)new = P(M, N)old + (1 - P(M,N)old) x Lenc,

Lenc is an initialization constant The predictabilities for all destinations D other

than N suffer ageing:P(M, D)new = P(M, D)old x γK,

γ is an aging constant K is a time factor

Transitive property updates the predictability of destination D for which N has a P(N, D) value:

P(M,D)new = P(M,D)old + (1 - P(M,D)old) x P(M,E) x P(E,D) x ββ is a scaling factor

The assumption here is that M is likely to meet N again.

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MAXPROP (1/2) Motivated by pedestrian mobility and city vehicles

(busses)(Burgess, et al. 2006)

Addressed resources issues considering vehicles Bulky equipment energy

Maintains ordered destination based queues Addresses on top of PRoPHET

• QoSQoS• Stale dataStale data

Assumes Unlimited buffer for own messages per node Fixed size buffer for relaying messages No topology knowledge/control

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MAXPROP (2/2) Communication steps (flooding-based!):1. Neighbor Discovery (no knowledge of when the next opportunity to communicate will be)

2. Data Transfer a) Transfer packets destined for neighbor peer, b) Transfer routing information, c) Acknowledge any delivered data,d) prioritize “young” relayed packets, e) Send un-transmitted packets by estimated delivery likelihood, f) ensure only new packets are sent.

3. Storage Management(expunge packets to accommodate the relay buffers)

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PARAMETRIC PROBABILISTIC ROUTING (1/2)

Developed for Sensor Networks(Barret, et al. 2005)

Based on controlled flooding: Packet forwarding decision by probability function Probability function is based on:

• distance to destination, distance to destination, • distance from original source to destination, distance from original source to destination, • number of copies already received, … number of copies already received, …

Variations:1. The Destination Attractor

Source-Destination distance and Current Relay-Destination distance2. Directed transmission

uses also the number of hops packet has already traveled.

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PARAMETRIC PROBABILISTIC ROUTING (2/2)

Estimating distances to Destination: Each sensor includes its current estimate of distance to D receiving such information, each sensor updates its distance

information A sensor chooses as S-D distance the minimum of the

currently received information from neighbors.

Potentially this leads to misinformation

Exponential scheme relaxes the problem, but enables wider flooding

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Fully context-aware routing protocol(Nguyen, et al. 2007)

Node Profile: nodes exchanging data must have some information about each other.

Selection of best forwarders: delivery probability based on the profile of the neighbors For every neighbor a sender calculates 2-hop route delivery

probability Forwards only if own delivery probability is less than a potential relay

Security considerations Assumptions for “community level” security (e.g. authentication,

signatures) Messages’ content is secure although the “evidences” of the node

profile can be recovered.

PROPICMAN

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Simulation framework for lower layer parameters inverstigation(Gazoni, et al. 2010)

Forwarding decision: Probability function based on modular metric

• DistanceDistance• ETXETX

Linear or piece wise• selection of shape and slope affects on the number of “certain forwarders”selection of shape and slope affects on the number of “certain forwarders”• can be varied upon execution to adapt to lossescan be varied upon execution to adapt to losses

Time to send Back-off based scheme implemented (with variable or fixed window

size) Highly probable forwarders get to transmit early.

Passive acknowledgements via overhearing

A FRAMEWORK FOR PROBABILISTIC ROUTING

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References A. Vahdat and D. Becker. Epidemic Routing for Partially-connected Ad

Hoc Networks. Technical Report: CS-200006, Duke University, April 2000. A. Lindgren, A. Doria, and O. Schelén. Probabilistic Routing in

Intermittently Connected Networks. In proc. of the 2003 ACM MobiHoc. J. Burgess, et al. MaxProp: Routing for vehicle-based disruption-tolerant

networks. In proc. of 2006 IEEE INFOCOM. C. L. Barrett et al. Parametric Probabilistic Routing in Sensor Networks,

Mobile Networks and Applications 10:4, pp 529-544, 2005. H. A. Nguyen, et al. Probabilistic Routing Protocol for Intermittently

Connected Mobile Ad Hoc Networks (PROPICMAN). In proc. of the 2007 IEEE WoWMoM.

Niki Gazoni, et al. A framework for opportunistic routing in multi-hop wireless networks. In proc. of the 2010 ACM PE-WASUN.

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Thanks for your attention!