the multi-agent system for dynamic network routing
DESCRIPTION
The Multi-agent System for Dynamic Network Routing. Ryokichi Onishi The Univ. of Tokyo, Japan. Contents. Related theme and paradigms MANET environment, AntNet, Miner ’ s Model Our proposal multiplying entries, evaluating entries Simulation and result effect of each model and formula - PowerPoint PPT PresentationTRANSCRIPT
The Multi-agent System The Multi-agent System for Dynamic Network for Dynamic Network RoutingRouting
Ryokichi OnishiRyokichi Onishi
The Univ. of Tokyo, JapanThe Univ. of Tokyo, Japan
ContentsContents
Related theme and paradigmsRelated theme and paradigmsMANET environment, AntNet, Miner’s MANET environment, AntNet, Miner’s
ModelModel
Our proposalOur proposalmultiplying entries, evaluating entriesmultiplying entries, evaluating entries
Simulation and resultSimulation and resulteffect of each model and formulaeffect of each model and formula
ConclusionConclusion
Computer and wirelessComputer and wireless
computer networkscomputer networks- decentralized management- decentralized management
wireless networkswireless networks- centralized management- centralized management
MANET environmentMANET environment
An epoch-making way of
wireless communication
multi-hopmulti-hop
wirelesswirelessad-hocad-hoc
peer-to-peerpeer-to-peer autonomousautonomous
How good is the MANET ?How good is the MANET ?
DBDB
interneinternett
DBDB
interneinternett
the MANETthe MANETenvironmentenvironment
the usualthe usualenvironmentenvironment
A basestation isn’t a must for their communicationA basestation isn’t a must for their communication more devices communicate with basestationsmore devices communicate with basestations
Miner’s Miner’s modelmodel
MA moves to a neighbor MA moves to a neighbor node the latest RA from node the latest RA from destination came through.destination came through.
the latest RA from Nodethe latest RA from Node Q Q
MAMA
Routing AgentRouting Agent
Message AgentMessage Agent
PP
AADD
CC BB
destinationdestination
sourcesource
Agent-based architectureAgent-based architecturefor wireless networkfor wireless network
AntNeAntNett
Ants follow & deposit pheromone trails.Ants follow & deposit pheromone trails.
pheromonepheromone frequencyfrequency
Pheromone trails are piled up on the Pheromone trails are piled up on the ground.ground.
quantityquantityThe rich food is, the more ants deposit.The rich food is, the more ants deposit.
freshnessfreshnesspheromone evaporates along time.pheromone evaporates along time.
Agent-based algorithmAgent-based algorithmfor wired networkfor wired network
Problems of AntNet Problems of AntNet
the blocking problemthe blocking problem If a good route is broken,If a good route is broken,
searching another route needs long time.searching another route needs long time. the shortcut problemthe shortcut problem
Even if a better route appeared,Even if a better route appeared,this new route is seldom discovered.this new route is seldom discovered.
Our routing agents walk randomly,Our routing agents walk randomly,and don’t follow pheromone trails.and don’t follow pheromone trails.
About our modelAbout our model
algorithm (mind)algorithm (mind) Making good routes in a sense of Making good routes in a sense of
probabilityprobabilityby ants’ path-finding modelby ants’ path-finding model
framework (body)framework (body) A simple decentralized managementA simple decentralized management
by multi-agent systemby multi-agent system
ContentsContents
Related theme and paradigmsRelated theme and paradigmsMANET environment, AntNet, Miner’s MANET environment, AntNet, Miner’s
ModelModel
Our proposalOur proposalmultiplying entries, evaluating entriesmultiplying entries, evaluating entries
Simulation and resultSimulation and resulteffect of each model and formulaeffect of each model and formula
ConclusionConclusion
Multiply entries Multiply entries (model example)(model example)
MA moves to a neighbor MA moves to a neighbor node the most EAs from node the most EAs from its destination came its destination came through.through.
MAMA
Explorer AgentExplorer Agent
Messenger AgentMessenger Agent
PP
AADD
CC BB
destinationdestination
sourcesource
three EAs from Node three EAs from Node QQ
Multiply entries Multiply entries (table example)(table example)
Pheromone trails are piled up on the ground.Pheromone trails are piled up on the ground. More route information from EAs are held in More route information from EAs are held in
the routing tables.the routing tables.
destdest nextnextNN AA
OO BB
PP nullnull
QQ AARR CC CC
AAnullnullDDAA
nextnext
CCAAnullnullCCAA
nextnext
CCBBnullnullCCDD
nextnext
CCRRAAQQnullnullPPBBOOAANN
nextnextdestdest
multiplied up to 4 entriesmultiplied up to 4 entries
new new oldold
a single entrya single entry
Evaluate entries Evaluate entries (model (model
example)example)
MA moves to a neighbor MA moves to a neighbor node which has the highest node which has the highest value of information on its value of information on its destination.destination.
Explorer AgentExplorer Agent
Messenger AgentMessenger Agent
PP
AADD
CC BB
destinationdestination
sourcesource
three EAs from Node three EAs from Node QQ
MAMA
Evaluate entries Evaluate entries (table example)(table example)
two attached sub-entriestwo attached sub-entries timetime the number of hopsthe number of hops
desdestt
nexnextt
nexnextt
nexnextt
nexnextt
NN AA DD AA AA
OO BB CC CC DD
PP nullnull nullnull nullnull nullnull
QQ AA BB AA AARR CC CC CC CC
destdestnextnext nextnext nextnext nextnext
timtimee
hophopss
timtimee
hophopss
timtimee
hophopss
timtimee
hophopss
NNAA DD AA AA
2828 33 2626 66 2525 33 1717 44
OOBB CC CC DD
2222 99 2121 33 1515 22 1212 33
PPnullnull nullnull nullnull nullnull
nullnull nullnull nullnull nullnull nullnull nullnull nullnull nullnull
QQ AA BB AA AA2828 1313 2626 22 2020 1111 1616 22
RRCC CC CC CC
3030 22 2525 22 2020 33 1010 22
Evaluate entries Evaluate entries (the way of (the way of evaluation)evaluation)
DDRRh-1h-1RR33RR22RR11SS
destination nodedestination node
Explorer AgentExplorer Agent
h
i EAh
iht
p 1
1
)1(The total reliabilityThe total reliability
source nodesource node
pp::the broken-link ratio a the broken-link ratio a timetime
tt::the time since info. the time since info. gottengotten
hh::#hops to the #hops to the destinationdestination
hhEAEA::#hops EAs move a #hops EAs move a
timetime
tp)1( EAht
p
1
)1(
EAht
p
2
)1(
EAh
ht
p
1
)1(
Ant metaphor and our modelAnt metaphor and our model
[ [ Ant metaphor ]Ant metaphor ]
Pheromone trails Pheromone trails are piled up on the are piled up on the ground.ground.
Pheromone trails Pheromone trails evaporate along evaporate along time.time.
The rich food is, the The rich food is, the more trails ants more trails ants deposit.deposit.
[ [ Our model ]Our model ]
Each next-node Each next-node entry is multiplied.entry is multiplied.
Next-node info. is Next-node info. is evaluated with evaluated with freshness sub-info.freshness sub-info.
Next-node info. is Next-node info. is evaluated with evaluated with distance sub-info.distance sub-info.
ContentsContents
Related theme and paradigmsRelated theme and paradigmsMANET environment, AntNet, Miner’s MANET environment, AntNet, Miner’s
ModelModel
Our proposalOur proposalmultiplying entries, evaluating entriesmultiplying entries, evaluating entries
Simulation and resultSimulation and resulteffect of each model and formulaeffect of each model and formula
ConclusionConclusion
Simulation Simulation (network model)(network model)
400400m squarem square
120m diameter120m diameter
Mobile NodeMobile Node100 [100 [units]units]3.6 [km/hr] const. vector3.6 [km/hr] const. vector60[60[m] radio wave rangem] radio wave range
Explorer AgentExplorer Agent100 [100 [units], move a secunits], move a secmovement history 10movement history 10random movementrandom movement
Gateway NodeGateway Node4 [4 [units], stationaryunits], stationaryinformation sourcesinformation sources60[m] radio wave range60[m] radio wave range
100100mm 100100mm200200mm
1 1 meter = 0.625 milemeter = 0.625 mile
Simulation Simulation (subject)(subject)
[ Performance Characteristics ][ Performance Characteristics ] ConnectivityConnectivity Route lengthRoute length
[ Compared Models ][ Compared Models ] 1 entry per a destination as Miner’s model1 entry per a destination as Miner’s model 60 entries per a destination as the first 60 entries per a destination as the first
modelmodel 20 entries with 40 sub-entries for 20 entries with 40 sub-entries for
evaluation evaluation as the second modelas the second model
the ideal modelthe ideal model
Result Result (The average connectivity over (The average connectivity over time)time)
Miner’s modelMiner’s model
the 1the 1stst proposal proposal
the 2the 2ndnd proposal proposalthe ideal modelthe ideal model
getting worse over timegetting worse over time
stable after 50 secondsstable after 50 seconds
Result Result (The average route length over (The average route length over time)time)
Miner’s modelMiner’s model
the 1the 1stst model model
the 2the 2ndnd model model
the ideal modelthe ideal model
getting worse over timegetting worse over time
stable after 50 secondsstable after 50 seconds
Result Result (average and standard (average and standard deviation)deviation)
ModelModelConnectivityConnectivity Route lengthRoute length
AveragAveragee
Std Std DevDev
AveragAveragee
Std Std DevDev
Miner’s Miner’s modelmodel 66%66% 9%9% 2.82.8 0.40.4
the 1st the 1st modelmodel 83%83% 7%7% 3.23.2 0.50.5
the 2nd the 2nd modelmodel 93%93% 5%5% 2.72.7 0.30.3
the the ideal ideal
modelmodel98%98% 2%2% 2.12.1 0.20.2
Result Result (The average connectivity over (The average connectivity over agents)agents)
the 2the 2ndnd model model
approaching to the idealapproaching to the ideal
ContentsContents
Related theme and paradigmsRelated theme and paradigmsMANET environment, AntNet, Miner’s MANET environment, AntNet, Miner’s
ModelModel
Our proposalOur proposalmultiplying entries, evaluating entriesmultiplying entries, evaluating entries
Simulation and resultSimulation and resulteffect of each model and formulaeffect of each model and formula
ConclusionConclusion
ConclusionConclusion
We proposed ants’ path finding algorithm We proposed ants’ path finding algorithm suitable for the MANET environment.suitable for the MANET environment.
It was proved that our model was proper, It was proved that our model was proper, because …because … our model showed better performance our model showed better performance
than Miner’s model.than Miner’s model. the more route information were gathered,the more route information were gathered,
the better routing performance was the better routing performance was improved.improved.
Future WorksFuture Works
breed our model breed our model compare our modelcompare our model
Thank you very much!Thank you very much!
Please get our paper Please get our paper and other related materials atand other related materials athttp://www.sail.t.u-tokyo.ac.jp/~ryo
[email protected]@sail.t.u-tokyo.ac.jp