1. A Probabilistic Routing Protocol for Mobile Ad Hoc Networks
Abdallah Jabbour • James Psota • Alexey Radul
{ajabbour, psota,
Jabbour, Psota, Radul
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2. presentation notes (delete this slide)
slides are currently not formatted; formatting will occur after content is more finalized; please don’t worry about that for now
total presentation time: ~15 minutes (5 min questions)  slides total
should give protocol a name!
need to add figures, but will talk to teammates first
may need to consolidate/merge slides…
I’d like to add an animation/diagram of the protocol in action
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3. Routing in Ad Hoc Networks
Most routing protocols…
Use fixed route to send all packets from a given source to a given destination
Send along path with minimum hop count
Use two main types of packets
Data packets
Control (routing) packets
<one reason why these are bad>
We think we can do better!
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4. Outline Related Routing Protocols Shortcomings of Related Protocols
DSDV, DSR, AODV
Probabilistic Routing Protocols
Shortcomings of Related Protocols
Protocol Description
Simulation Overview and Results
Conclusion
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5. Related Routing Protocols
Destination-Sequenced Distance Vector
Loop-free, hop-by-hop distance vector
Routes prioritized by sequence numbers
Dynamic Source Routing
Employs source routing
Floods route requests
Maintains routes by link breakage notification
Ad Hoc On-Demand Distance Vector
Combines sequence numbers from DSDV and Route Discovery from DSR
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6. Probabilistic Routing Protocols
Control and routing information (“ants”) sent randomly
Data forwarded deterministically to path with best metric
Examples
Ant-Based Control (ABC)
AntNet
Ant-Colony-Based Routing Algorithm (ARA)
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7. Drawbacks and Limitations of Above Protocols
Routing packets hinder performance
Decreases available bandwidth
Increases transmission latency
High recovery latency due to static routes
DSDV, DSR, AODV
Probabilistic protocols incorrectly assume symmetric traffic
Above protocols use shortest hop routes
Tends to choose routes with less capacity than optimal ones
Tends to use marginal links
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8. Our Protocol’s Design Goals
Minimize routing packets
Especially those interfering with traffic
Use a better measure of network state than minimum hop count to make forwarding decisions
Better cope with link outages
our goals are to increase performance by…
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9. Protocol Overview
Minimize control packets by prepending protocol-level headers onto all data packets
Both when originating and forwarding a packet
Nodes promiscuously listen to all traffic, using protocol headers to update state
Base routing decisions on link loss ratios
Modular in both choice of metric and metric to probability mapping
Use ETX instead of minimum hop count
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10. Random Routing
Nodes forward probabilistically to neighbor ni with probability pi n1
routing table
p1 = 0.1
dest p1 p2 p3 d
0.1
0.4
0.5
dest p1 p2 p3 d
0.1
0.4
0.5
dest p1 p2 p3 d
0.1
0.4
0.5 s n2 d
p1 = 0.4
p3 = 0.5 n3
Route is not fixed, so packets can still reach destination immediately upon link breakage
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11. Random Routing
Nodes forward probabilistically to neighbor ni with probability pi n1
routing table
p1 = 0.3
dest p1 p2 p3 d
0.1
0.3
0.4
0.0
0.5
0.7 x s n2 d
p1 = 0.4 x x x
link breaks!
p3 = 0.7 n3
Update forwarding probability upon link breakage (nodes see infinite loss ratio on link)
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12. Deterministic Routing
Forward ALL packets along path with best metric
Our flexible infrastructure allowed simulation of both
First to compare Random to Deterministic Routing
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13. Node State Nodes maintain the following state
Dynamically-updated set of neighbors
Loss ratios to and from each neighbor
Routing state
Metric values for each destination and each neighbor-destination pair
Probability of forwarding to a certain neighbor in order to reach a desired destination
Requests and fulfillments thereof for information about destinations
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14. Protocol Header Contents
Each outgoing packet contains a protocol-level header
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15. State Update Nodes update state Probability Distribution Updates
Upon sending
Upon receiving
Periodically
Refresh stale state and alert neighbors that you’re still alive
Probability Distribution Updates
Probability distribution and metric values updated along with other node state
Values evolve in response to nodes entering and leaving the system and changes in link quality
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16. Simulation Environment
50 mobile nodes in 1500m x 300m area
Random waypoint movement model
900s simulation time
We investigated…
Pause times
Node speeds
Connection patterns
Packet sizes
Used 30 CBR UDP sources
Avoid TCP because YYY
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17. Evaluation Measures Packet delivery ratio Packet delivery latency
Goodput
Number of bytes of overhead
Path length optimality
Transmission overhead
Route acquisition latency
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18. Simulation Results
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19. Conclusions
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20. insert title slide
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21. Backup Slides
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22. Implementation Overview
ns-2 with Monarch mobility extensions used to model mobile ad hoc network
Implemented protocol in C++
Data generated by 30 CBR UDP sources
Metrics
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23. Routing and Distribution Updates
Random Routing
Each node sends probabilistically to neighbor i with probability pi for destination d
Deterministic Routing
Each node sends exclusively to neighbor i when probability pi for destination d is highest
Our flexible infrastructure allowed simulation of both
Jabbour, Psota, Radul
6.829 Final Project