Swarm Intelligent Networking Martin Roth Cornell University Wednesday, April 23, 2003.

Swarm Intelligent Networking

Martin RothCornell UniversityWednesday, April 23, 2003

What is Swarm Intelligence?

Swarm Intelligence (SI) is the local interaction of many simple agents to achieve a global goalEmergence

Unique global behavior arising from the interaction of many agents

Stigmergy Indirect communication

Generally through the environment

Properties of Swarm Intelligence

Properties of Swarm Intelligence are:Agents are assumed to be simple Indirect agent communicationGlobal behavior may be emergent

Specific local programming not necessaryBehaviors are robust

Required in unpredictable environments Individuals are not important

Swarm Intelligence Example

The food foraging behavior of ants exhibits swarm intelligence

Principles of Swarm Intelligence

What makes a Swarm Intelligent system work?

Positive FeedbackNegative FeedbackRandomnessMultiple Interactions

SI: Positive Feedback

Positive Feedback reinforces good solutions

Ants are able to attract more help when a food source is found

More ants on a trail increases pheromone and attracts even more ants

SI: Negative Feedback

Negative Feedback removes bad or old solutions from the collective memory

Pheromone DecayDistant food sources are exploited last

Pheromone has less time to decay on closer solutions

SI: Randomness

Randomness allows new solutions to arise and directs current ones

Ant decisions are randomExploration probability

Food sources are found randomly

SI: Multiple Interactions

No individual can solve a given problem. Only through the interaction of many can a solution be found

One ant cannot forage for food; pheromone would decay too fast

Many ants are needed to sustain the pheromone trail

More food can be found faster

Swarm Intelligence Conclusion

SI is well suited to finding solutions that do not require precise control over how a goal is achieved

Requires a large number of agentsAgents may be simpleBehaviors are robust

SI applied to MANETs

An ad hoc network consists of many simple (cooperative?) agents with a set of problems that need to be solved robustly and with as little direct communication as possible

Routing is an extension of Ant Foraging! Ants looking for food… Packets looking for destinations…

Can routing be solved with SI? Can routing be an emergent behavior from the

interaction of packets?

SI Routing Overview

Ant-Based ControlAntNetMobile Ants Based RoutingAnt Colony Based Routing AlgorithmTermite

Ant-Based Control Introduction

Ant Based Control (ABC) is introduced to route calls on a circuit-switched telephone networkABC is the first SI routing algorithm for

telecommunications networks1996

ABC: Overview

Ant packets are control packets Ants discover and maintain routes

Pheromone is used to identify routes to each node Pheromone determines path probabilities

Calls are placed over routes managed by ants Each node has a pheromone table maintaining

the amount of pheromone for each destination it has seen Pheromone Table is the Routing Table

ABC: Route Maintenance

Ants are launched regularly to random destinations in the network

Ants travel to their destination according to the next-hop probabilities at each intermediate nodeWith a small exploration probability an ant

will uniformly randomly choose a next hopAnts are removed from the network

when they reach their destination

ABC: Routing Probability Update

Ants traveling from source s to destination d lay s’s pheromoneAnts lay a pheromone trail back to their

source as they movePheromone is unidirectional

When a packet arrives at node n from previous hop r, and having source s, the routing probability to r from n for destination s increases

ABC: Routing Probability Update

p determined by age of packetProbabilities remain normalized

p

ppp sr

sr

1,

,

p

pp srn

srn

1,

,

ABC: Route Selection (Call Placement)

When a call is originated, a circuit must be established

The highest probability next hop is followed to the destination from the source

If no circuit can be established in this way, the call is blocked

ABC: Initialization

Pheromone Tables are randomly initialized

Ants are released onto the network to establish routes

When routes are sufficiently short, actual calls are placed onto the network

ABC Conclusion

Only the highest probability next hop is used to find a route

Probabilities are changed according to current values and age of packet

Reference

R. Schoonderwoerd, O. Holland, J. Bruten, L. Rothkranz, Ant-based load balancing in telecommunications networks, 1996.

SI Routing Overview


AntNet Introduction

AntNet is introduced to route information in a packet switched network

AntNet is related to the Ant Colony Optimization (ACO) algorithm for solving Traveling Salesman type problems

AntNet Overview

Ant packets are control packetsPackets are forwarded based on next-

hop probabilitiesAnts discover and maintain routes

Internode trip times are used to adjust next-hop probabilities

Ants are sent between source-destination pairs to create a test and feedback signal system

AntNet Route Maintenance(F)

Forward Ants, F, are launched regularly to random destinations in the network

F maintains a list of visited nodes and the time elapsed to arrive there Forward Ant packet grows as it moves through the network Loops are removed from the path list

F is routed according to next-hop probability maintained in each node’s routing table A uniformly selected next hop is chosen with a small

exploration probability If a particular next hop has already been visited, a uniformly

random next hop is chosen

AntNet Route Maintnence(B)

When F arrives at its destination, a Backward Ant, B, is returned to the source

B follows the reverse path of F to the source At each node, B updates the routing table

Next-hop probability to the destination Trip time statistics to the destination

Mean Variance

AntNet Routing

Data packets are routed using the next-hop probabilities

Forward ants are routed at the same priority as data packetsForward Ants experience the same

congestion and delay as dataBackward ants are routed with higher

priority than other packets

AntNet Conclusion

AntNet is a routing algorithm for datagram networks

Explicit test and feedback signals are established with Forward and Backward Ants

Routing probabilities are updated according to trip time statistics

AntNet Reference

G. Di Caro, M. Dorigo, Mobile Agents for Adaptive Routing, Technical Report, IRIDIA/97-12, Universit Libre de Bruxelles, Beligium, 1997.

SI Routing Overview


Mobile Ants-Based Routing Intro

Mobile Ants-Based Routing (MABR) is a MANET routing algorithm based on AntNet

Location information is assumedGPS

MABR Overview

MABR consists of three protocols:Topology Abstracting Protocol (TAP)

Simplifies network topologyMobile Ants-Based Routing (MABR)

Routes over simplified topologyStraight Packet Forwarding (SPF)

Forward packets over simplified topology

MABR: Topology Abstracting Protocol

TAP generates a simplified network topology of logical routers and logical links

All individual nodes are part of a logical router depending on their locationA single routing table may be distributed

over all nodes that are part of a logical router

MABR: TAP

•Zones are created, each containing more logical routers than the last

•Zones are designated by their location

•Logical links are defined to these zones

MABR Routing

An AntNet-like protocol with Forward and Backward ants is applied on the logical topology supplied by TAP

Forward ants are sent to random destinations Ants are sent to the zones containing these

destinations Ants collect path information during their trip

Backward ants distribute the path information on the way back their source Logical link probabilities are updated

MABR: Routing

MABR: Straight Packet Forwarding

Straight Packet Forwarding is responsible for moving packets between logical routers

Any location based routing protocol could be used

MABR is responsible for determining routes around holes in the networkSPF should not have to worry about such

situations

MABR Conclusion

The network topology is abstracted to logical routers and links TAP

Routing takes place on the abstracted topology MABR

Packets are routed between logical routers to their destinations SPF

MABR is still under development Results are not yet available

SI Routing Overview


Ant Colony Based Routing Overview

Ant-Colony Based Routing (ARA) uses pheromone to determine next hop probability

Employs a flooding scheme to find destinations

ARA Route Discovery

To discover a route: A Forward Ant, F, is

flooded through the network to the destination

A Backward Ant, B, is returned to the source for each forward ant received

ARA Route Discovery

Reverse routes are automatically established as forward ants move through the network

Backward ants reinforce routes from destination to source

ARA Routing

Next Hop Probabilities are determined from the pheromone on each neighbor link

N

idi

dndn

P

Pp

1,

,,

ARA Pheromone Update

When a packet is received from r at n with source s and destination d:

r updates its pheromone table

n updates its pheromone table

dndn PP ,,

srsr PP ,,

ARA Pheromone Decay

Pheromone is periodically decayed according to a decay rate,

ePP dndn ,, Nn Dd

ARA Loop Prevention

Loops may occur because route decisions are probabilistic

If a packet is received twice, an error message is returned to the previous hopPackets identified based on source address

and sequence numberThe previous hop sets Pn,d = 0

No more packets to destination d will be sent through next hop n

ARA Route Recovery

A route error is recognized by the lack of a next-hop acknowledgement

The previous hop node sets Pn,d = 0

An alternative next hop is calculated If no alternative next hop exists, the packet

is returned to previous hopA new route request is issued if the data

packet is returned to the source

ARA Conclusion

ARA is a MANET routing algorithmFlooding is used to discover routesAutomatic retransmit used to recover

from a route failurePacket backtracking used if automatic

retransmit failsNext Hop probability proportional to

pheromone on each link

ARA Reference

M. Gunes, U. Sorges, I. Bouaziz, ARA – The Ant-Colony Based Routing Algorithm for MANETs, 2003.

SI Routing Overview


Termite Overview

Termite is a MANET routing algorithmTermite uses pheromone to produce

next-hop probabilitiesRandom routing

Termite aims to reduce control trafficTermite should scale across network

size and volatility

Termite Routing

Each packet is forwarded probabilistically based on the amount of destination pheromone on each neighbor link

F, K used to tune the routing probabilities No packet is routed out the link it arrived on

N

i

Fdi

Fdn

dn

KP

KPp

1,

,,

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Termite Pheromone Update

When a packet arrives at a node n from previous hop r originally from source s, n updates it Pheromone Table

srsr PP ,,

Termite Pheromone Decay

Pheromone is periodically decayed according to a decay rate,

ePP dndn ,, Nn Dd

Termite Route Recovery

If a transmission to a neighbor fails:The neighbor is removed from the

Pheromone TableAn alternative next-hop is calculated and

the packet is resentIf no alternative exists, the packet is

dropped

Termite Route Discovery(RREQ)

If a node does not contain a needed destination in its pheromone table, a route request is issued

A route request (RREQ) packet follows a random walk through the network until a node is encountered containing some destination pheromoneA route reply (RREP) is returned to the

source

Termite Route Discovery(RREP)

A route reply (RREP) packet follows the pheromone trail normally back to the RREQ sourceThe source of the RREP is the requested

node, regardless of which node actually originates the packet

The requested node’s pheromone is automatically spread through the network

Termite

Termite minimizes control traffic by allowing all packets to explore the network

Path discovery uses random walkRoute Discovery packets are unicast

Open Issues

Termite still has many open questions How to automatically determine routing

parameters based on local information Decay rate, Seed rate and distance Number of RREQs per Route Request

How good is random walk route discovery

How exactly are the various parameters related? Can some be determined from others? How do they affect performance?

Simulation Implementation

Simulation Environment

•10 m transmission radius

•1 Mbps channel

•64B data packets

•CBR source

•2 packets per second with acknowledgement

Network Performance vs. Mobility

Path Length vs. Mobility

Next Hop PDF vs. Mobility

Termite Reference

M. Roth, S. Wicker, Termite: Emergent Ad-Hoc Networking, 2003.

SI Advantages

SI based algorithms generally enjoy: Multipath routing

Probabilistic routing will send packets all over the network

Fast route recovery Packets can easily be sent to other neighbors by

recomputing next-hop probabilities Low Complexity

Little special purpose information must be maintained aside from pheromone/probability information

More SI Advantages

ScalabilityAs with any colonies numbering in the

millions, SI algorithms can potentially scale across several orders of magnitude

Distributed AlgorithmSI based algorithms are inherently

distributed

SI Disadvantages

SI also suffers from:Directional Links

Bidirectional links are generally assumed by using reverse paths

NoveltySI is a relatively new approach to routing. It

has not been characterized very well, analytically

Swarm Intelligence Conclusion

The fundamental idea behind using SI for routing in MANETs is to use the interaction of many packets to generate routing tables while minimizing the use of explicit routing packets

The arrival of packets is observed, which influences next-hop routing probabilitiesCritical packets may include specialized ant

packets or all packets

Swarm Intelligent Networking Martin Roth Cornell University Wednesday, April 23, 2003.

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