Routing in Opportunistic Networks
Chapter 8:
Probabilistic Routing Schemes
for Ad-Hoc Opportunistic Networks
1Vangelis
Angelakis, 2Elias Tragos ,
3George Perantinos, and 1Di Yuan
1 Linköping
University, Sweden
2 Foundation for Research and Technology –Hellas
3 Forthnet S.A.
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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 1,2
 Scheduling takes care of 3 once 1 and 2 have been decided
 Relaying typically assumes:
 Some topology knowledge
 Collaborating 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 transmission
2. Delay\Disruption -Tolerant Networking
 Problems arising from opportunistic communication:
1. Topology is becoming too variable
2. 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 and
• When 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 and
• When 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
1
SVA
2
A
Request: (SVA+SVB’)
3
B
Messages unknown to B
 Per-host queuing
 New messages given preference over old ones in
terms of buffer availability
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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 β
β 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
• QoS
• Stale 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)
b)
c)
d)
e)
f)
Transfer packets destined for neighbor peer,
Transfer routing information,
Acknowledge any delivered data,
prioritize “young” relayed packets,
Send un-transmitted packets by estimated delivery likelihood,
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 from original source to destination,
• number of copies already received, …
 Variations:
1. The Destination Attractor
 Source-Destination distance and Current Relay-Destination distance
2. 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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PROPICMAN
 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.
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A FRAMEWORK FOR PROBABILISTIC ROUTING
 Simulation framework for lower layer parameters inverstigation
(Gazoni, et al. 2010)
 Forwarding decision:
 Probability function based on modular metric
• Distance
• ETX
 Linear or piece wise
• selection of shape and slope affects on the number of “certain forwarders”
• can 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
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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!
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