termite: a swarm intelligent routing algorithm for mobile
TRANSCRIPT
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Termite:A Swarm Intelligent Routing Algorithmfor Mobile Wireless Ad-Hoc Networks
Martin RothWireless Intelligent Systems LaboratoryCornell UniversityIthaca, New YorkUSA
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermiteMotivation and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
ConclusionFuture WorkContributions
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermiteMotivation and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
ConclusionFuture WorkContributions
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Introduction to Ad-Hoc Networks
A Mobile WirelessAd-Hoc Network is:A network of mobile nodes that communicate via a wireless channel, cooperatively forwarding information
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Ad-Hoc Routing Problem
Network Topology is very unstable
Node movementChanges in Communications ChannelNode loss
Node deathLoss of Power
How can data be reliably moved from Source to Destination?
Maximize Data GoodputMinimize Control Traffic
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Typical Solutions to Ad-Hoc Routing
Ad-Hoc Routing exists since the early 1990s
DSR, AODV, ZRP, TORA, DSDV, PARO, etc…Same general approach
Flood to collect informationGraph theoretical computationRoute!
Drawbacks of typical approachesControl traffic for path recoveryPath maintenance requires complicated rulesHigh mobility causes too much control traffic; network becomes unusable
!!!
***
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Swarm Intelligent Routing
Instead of building explicit paths,Let each path have a probability of being used
Link failures may be immediately circumvented by choosing the next most probable path
Control traffic is avoided
Swarm Intelligent (SI) Routing is a method for building probabilistic paths between nodes based on simple rules
Simple probability update rules avoid algorithm complexity
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Swarm Intelligence
A framework for designing distributed algorithmsOriginally derived from models of social insect behavior
System of many independent agents obeying:Positive Feedback
Reinforce good solutions
Negative FeedbackRemove old solutions
RandomnessAllows many solutions to be tested simultaneously
Multiple InteractionsSpreads local informationGenerates global behavior from local interactions
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Swarm Intelligence:Ant Foraging
[1] http://iridia.ulb.ac.be/~mdorigo/ACO/RealAnts.html
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermitePrevious Work and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
ConclusionFuture WorkContributions
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A Brief History ofSwarm Intelligent Routing
Ant Based Control1996
AntNet1997
Cooperative Asymmetric Forwarding1998
Virtual-Wavelength Path Routing1999
Probabilistic Emergent Routing Algorithm2002
Mobile Ants-Based Routing2002
Multiple Ant Colony Optimization2002
Ant-like Routing Algorithm2002
Ad-hoc Networking with Swarm Intelligence2004
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Termite
TermiteSwarm intelligent routing algorithm formobile wireless ad-hoc networksDesigned with swarm intelligence framework
Reduce control trafficCollect network information from overheard packets
Reduce complexity of an individual nodeModel all routing functions mathematically
Increase robustness to changes in network topologyEmergent routing behavior from node interactions
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Termite:A SI MANET Routing Algorithm
Each node has a Pheromone TableList of neighbors and destinations
Packet arrives at APrevious hop: B
Source Pheromone decaysPheromone, γ, is addedto link with BRoute more likely through BDestination → Source
Destination
Source
A
BC
E
S DD
BC
?
Pheromone Table
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The Mathematics behind Termite
Pheromone AccountingClassic pheromone update model
Continuous Pheromone Decay
Forwarding Equation
Pheromone Threshold, KPheromone Sensitivity, F
( )( )∑
∈
+
+=
nNj
Fndj
Fndin
diKP
KPp
,
,.
( )τnsttnsi
nsi
n ePPNi −−⋅←∈∀ ,,,γ+← n
srnsr PP ,,
normalize!
Destinations
Nei
ghbo
rs
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Route Management
Route Management is the finding, repairing, and updating of pathsRoute Discovery
Route Request (RREQ)Random walk to find destination
Not floodingRoute Reply (RREP)
Routed to the source according to theForwarding Equation
Same as data packetsControl packets are forwarded with high priority
Route RepairLost neighbor removed from Pheromone TablePacket resent on a remaining link
Neighbors are gained by overhearing transmissions
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Termite Simulations:Goodput vs. Node Mobility
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Termite Simulations:Mean Packet Path Length
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Termite Conclusions
Large data packets explore the networkNot energy efficientNot bandwidth efficientOnly a constant fraction of control packets needed
Nodes maintain knowledge of most destinationsBasic routing decisions can be made withoutresorting to control trafficRoutes can be repaired automatically
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermiteMotivation and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
ConclusionFuture WorkContributions
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Motivation
The Termite concept has been shown to workHow can performance be improved?
Start with an analytical approach
Questions to Answer:How does pheromone behave on a link?
What parameters are involved?What are the global effects of this behavior?
How do local parameters affect global behavior?
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The Model
[2] D. Subramanian, P. Druschel, J. Chen, Ants and Reinforcement Learning: A Case Study in Routing in Dynamic Networks, Proceedings of the International Joint Conference on Artificial Intelligence, 1997.
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Analysis Approach
Single link caseGain intuition onrelevant parameters
Two link systemExplore system behavior
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Single Link Pheromone
Packets arrive with poisson distributionExpected pheromone over time is calculated
A scale invariant parameter is found!
−
−+−
=−
βµ
βµ β
τ
11)( 0PetEP
t
τλλβ+
=
( )τ
τλµβ
µ +=
−=1
EP
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Pheromone Filter
Linear filter analogyPheromone arrival equation…
…can be expressed as a summation…
…which can be expressed as a convolution…
…of a one-tap infinite impulse response (IIR) filterThe pheromone update rule is a time dependant linear IIR filter
Commonly used for averagingBut this filter is unnormalized
∑ −
=−+=
1
00 )()( n
iin inPnEP βγβ
)()()( nBnnEP ∗= γ nnB β=)(
( )( )( ) µµµ τττ +⋅⋅+⋅+= −−− nttt eeePnP K210)(
-3 -2 -1 0 1 2 30
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2Frequency Response of IIR Filter (β = 0.5)
Normalized Frequency
Gai
n
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Two Link Pheromone
Write a system of equations to describe the pheromone on links
Numerically find the steady state solution
All pheromone ends up on the better link
As expected…
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γ Pheromone Filter
γ Pheromone Filter is the classic ant model
And the maximum mean link pheromone
Per link pheromone can be determined
( )0,0
~ µλτ
τλλ
A
ABABP
+=
0,0,0,0 µλλ
τλλ B
ABA
BA
AB pPP
+⋅
+
←
( )τnsttnsi
nsi
n ePPNi −−⋅←∈∀ ,,,γ+← n
srnsr PP ,,
100
101
102
0
0.5
1
1.5
2
2.5
time [# packet arrivals]
Phe
rom
one
on L
ink
Link Pheromone vs. Time (γ Pheromone Filter)
Dominant Link @ A (µ1 = 1)
Lesser Link @ A (µ0 = 0.99)
Dominant Link @ B (µ1 = 1)
Lesser Link @ B (µ0 = 0.99)
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Normalized γ Pheromone Filter
Joint Decay IIR Filter is a normalized γ pheromone filter
And the maximum mean link pheromone
Per link pheromone can be determined
( )( ) 0,0
~ µτλλτλλ
AB
ABABP +
+=
( )τnsttnsi
nsi
n ePPNi −−⋅←∈∀ ,,,( )[ ] γτ ⋅−+← −− srttn
srnsr ePP ,1,,
100
101
102
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
time [# packet arrivals]
Phe
rom
one
on L
ink
Link Pheromone vs. Time (Joint Decay IIR Averaging Filter)
Dominant Link @ A (µ1 = 1)
Lesser Link @ A (µ0 = 0.99)
Dominant Link @ B (µ1 = 1)
Lesser Link @ B (µ0 = 0.99)
0,0,0
,0,0,0 1 µ
τλλ
λλ
τλλ B
AAB
BA
BBABA
BA
AB p
pp
PP
+−
+⋅
+
←
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How to Optimize Performance
Independent variables must be identifiedThe only controllable parameter isthe pheromone decay rate, τ
Packet arrival rate, λ, is givenNetwork environment changes on its own accord
Performance can only be optimized through manipulation of τ
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On the Pheromone Decay Rate
Decay rate intuitionτ > τ*, information about network is lost too quicklyτ < τ*, too much information is retained
Order of magnitude studies to confirm decay rate trendPerformance improves with larger τ as network becomes more volatile
1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5Pheromone Decay Rate (τ) vs. Speed
Node Speed (m/s)
Bes
t S
uite
d P
hero
mon
e D
ecay
Rat
e
Joint Decay IIR Filterγ Pheromone FilterpDijkstra
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Decay Rate Heuristic:Maximum Pheromone
Link pheromone should decay within network correlation time
Maximum pheromone is known!
KeP ct =− τ~
Ke PFct =⋅⋅+ − γτµττλ
2
Ke PFct =⋅⋅++ − γτµ
τλτλ2
0 1 2 3 4 5 6 70
1
2
3
4
5
6
7
8
9
10
Instance Rate (sec-1)
Phe
rom
one
Dec
ay R
ate,
τ
τ Heuristic vs. Network Instance Rate
γ Pheromone FilterJoint Decay IIR Filter
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Decay Rate Heuristic:Filter Cutoff
-3 -2 -1 0 1 2 30
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2Frequency Response of IIR Filter (β = 0.5)
Normalized Frequency
Gai
n
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Instance Rate (sec-1)
Phe
rom
one
Dec
ay R
ate,
τ
τ Heuristic vs. Network Event Instance Rate
γ Pheromone FilterNormalized γ Pheromone FilterPheromone Filter Cutoff (-0.5dB)Pheromone Filter Cutoff (-3dB)Pheromone Filter Cutoff (-10dB)
Pheromone update can be viewed as a simple averaging filer
Decay rate can be adjusted to control the cutoff point of the filter
Depends also on packet arrival rate
Ultimately too complicated for a serious implementation
ωτλβω je
B −⋅−=
),(11)(
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermiteMotivation and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
ConclusionFuture WorkContributions
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Motivation
How well do update strategies actually work?What is the global effect of a pheromone update method?
Data goodputDelivery efficiency
What effect do parameters have onglobal performance?
Exploration vs. Exploitation
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Termite Enhancements
Several improvements are made to the routingTrue Continuous Pheromone Decay
Decay based on pheromone observation interarrival timesDecay on packet arrival and forwarding
Source Pheromone Repel [3]
R: pheromone repel constant
( )( )∑
∈
−
−
=
nNj
Rnsj
ndj
Rnsi
ndi
dipp
ppp
,,
,,,ˆ
[3] G. N. Varela, M. C. Sinclair, Ant Colony Optimization for Virtual Wavelength Path Routing and Wavelength Allocation, Proceedings of the Congress of Evolutionary Computation, 1999.
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Pheromone Update Methods
Traditional pheromone based approachesγ Pheromone FilterNormalized γ Pheromone FilterClassic Pheromone FilterProbabilistic DijkstraAnt-Based Control + X
Independent link utility estimationIIRBox
Control casesOracleRandom Routing
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Data Goodput vs. Node Speed
1 2 3 4 5 6 7 8 9 100.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1Goodput vs. Speed
Node Speed (m/s)
Goo
dput
BoxIIRJoint Decat IIR FilterOracleγ Pheromone FilterClassic Pheromone FilterpDijkstraRandom RoutingABC + pDijkstraABC + γ Pheromone FilterABC + IIR2
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Path Inefficiency vs. Node Speed
1 2 3 4 5 6 7 8 9 101
1.5
2
2.5
3
3.5Average Path Overshoot Ratio vs. Speed
Node Speed
Ave
rage
Pat
h O
vers
hoot
Rat
io
BoxIIRJoint Decat IIR FilterOracleγ Pheromone FilterClassic Pheromone FilterpDijkstraRandom RoutingABC + pDijkstraABC + γ Pheromone FilterABC + IIR2
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Delivery Efficiency vs. Node Speed
1 2 3 4 5 6 7 8 9 100.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65Goodput/Average Path Overshoot Ratio vs. Speed
Node Speed (m/s)
Goo
dput
/ A
vera
ge P
ath
Ove
rsho
ot R
atio
BoxIIRJoint Decat IIR FilterOracleγ Pheromone FilterClassic Pheromone FilterpDijkstraRandom RoutingABC + pDijkstraABC + γ Pheromone FilterABC + IIR2
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Pheromone Accouting:Performance Evaluation
γ pheromone filter achieves best goodputThe algorithm nature intended!
Normalized γ pheromone filter achievesbest overall performance
Less pheromone allows faster adaptationIndependent link estimators perform badly
Box, IIRInformation loss on all links must be accounted for
The Oracle does not perform perfectly
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Overview
IntroductionMobile Wireless Ad-Hoc NetworksSwarm Intelligence
TermiteMotivation and IntroductionPerformance Evaluation
Pheromone Model AnalysisPheromone Behavior in MANET ModelReinforcement LearningParameter Heuristics
Pheromone Update SchemesEnhancementsSimulations
AardvarkResimulation
ConclusionFuture Work
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Conclusion
Created SI Termite routing algorithmUses Pheromone Based MetricRandom Walk for Route DiscoveryLocal Retransmit instead of Local Repair
Developed Pheromone Decay Rate HeuristicsMaximum PheromoneFilter Cutoff
Developed Pheromone Accounting SchemesNormalized γ Pheromone FilterProbabilistic Bellman-Ford
Updated Termite with new FeaturesContinuous Pheromone DecaySource Pheromone RepelUpdated Pheromone Accounting Schemes
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Future Work
TermiteAdapt Termite to asymmetric networks
Use Cooperative-Asymmetric Forwarding (CAF) [4]
Use flooding for route discovery?Further explore operational limits
RoutingSystem of linear systems
Extend the linear filter analogyNetwork correlation time
Swarm IntelligenceFormalization of information and information loss
[4] M. Heusse, D. Snyers, S. Guerin, P. Kuntz, Adaptive Agent-Driven Routing and Load Balancing in Communication Networks, 1998.
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Publications
M. Roth, S. Wicker,Ad-Hoc Networking with Swarm Intelligence,in preparation, 2005.M. Roth, S. Wicker,Termite: Updates and Comparisons in Swarm Intelligent Ad-Hoc Networking,in preparation, 2005.M. Roth, S. Wicker,Performance Evaluation of Pheromone Update Schemesin Swarm Intelligent MANETs,Under Review, 2005.M. Roth, S. Wicker,Asymptotic Pheromone Behavior in Swarm Intelligent MANETs:An Analytical Analysis of Routing Behavior,Sixth IFIP IEEE International Conference on Mobile and Wireless Communications Networks (MWCN), 2004. M. Roth, S. Wicker,Termite: Ad-Hoc Networking with Stigmergy,IEEE Globecom 2003, 2003.M. Roth, S. Wicker,Termite: Emergent Ad-Hoc Networking,The Second Mediterranean Workshop on Ad-Hoc Networks, 2003.
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Thank you!
Questions?