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Swarm Intelligent Networking. Martin Roth Cornell University Wednesday, April 23, 2003. What is Swarm Intelligence?. Swarm Intelligence (SI) is the local interaction of many simple agents to achieve a global goal Emergence Unique global behavior arising from the interaction of many agents

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Swarm intelligent networking

Swarm Intelligent Networking

Martin Roth

Cornell University

Wednesday, April 23, 2003


What is swarm intelligence
What is Swarm Intelligence?

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

    • Emergence

      • 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

  • Properties of Swarm Intelligence are:

    • Agents are assumed to be simple

    • Indirect agent communication

    • Global behavior may be emergent

      • Specific local programming not necessary

    • Behaviors are robust

      • Required in unpredictable environments

    • Individuals are not important


Swarm intelligence example
Swarm Intelligence Example

The food foraging behavior of ants exhibits swarm intelligence


Principles of swarm intelligence
Principles of Swarm Intelligence

What makes a Swarm Intelligent system work?

  • Positive Feedback

  • Negative Feedback

  • Randomness

  • Multiple Interactions


Si positive feedback
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
SI: Negative Feedback

Negative Feedback removes bad or old solutions from the collective memory

  • Pheromone Decay

  • Distant food sources are exploited last

    • Pheromone has less time to decay on closer solutions


Si randomness
SI: Randomness

Randomness allows new solutions to arise and directs current ones

  • Ant decisions are random

    • Exploration probability

  • Food sources are found randomly


Si multiple interactions
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
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 agents

  • Agents may be simple

  • Behaviors are robust


Si applied to manets
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
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Si routing overview1
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Ant based control introduction
Ant-Based Control Introduction

  • Ant Based Control (ABC) is introduced to route calls on a circuit-switched telephone network

    • ABC is the first SI routing algorithm for telecommunications networks

      • 1996


Abc overview
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
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 node

    • With a small exploration probability an ant will uniformly randomly choose a next hop

  • Ants are removed from the network when they reach their destination


Abc routing probability update
ABC: Routing Probability Update

  • Ants traveling from source s to destination d lay s’s pheromone

    • Ants lay a pheromone trail back to their source as they move

    • Pheromone 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 update1
ABC: Routing Probability Update

  • Dp determined by age of packet

  • Probabilities remain normalized


Abc route selection call placement
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
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
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
Reference

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


Si routing overview2
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Antnet introduction
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
AntNet Overview

  • Ant packets are control packets

  • Packets are forwarded based on next-hop probabilities

  • Ants 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
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
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
AntNet Routing

  • Data packets are routed using the next-hop probabilities

  • Forward ants are routed at the same priority as data packets

    • Forward Ants experience the same congestion and delay as data

  • Backward ants are routed with higher priority than other packets


Antnet conclusion
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
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 overview3
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Mobile ants based routing intro
Mobile Ants-Based Routing Intro

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

  • Location information is assumed

    • GPS


Mabr overview
MABR Overview

MABR consists of three protocols:

  • Topology Abstracting Protocol (TAP)

    • Simplifies network topology

  • Mobile Ants-Based Routing (MABR)

    • Routes over simplified topology

  • Straight Packet Forwarding (SPF)

    • Forward packets over simplified topology


Mabr topology abstracting protocol
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 location

    • A single routing table may be distributed over all nodes that are part of a logical router


Mabr tap
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
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 straight packet forwarding
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 network

    • SPF should not have to worry about such situations


Mabr conclusion
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 overview4
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Ant colony based 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
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 discovery1
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
ARA Routing

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


Ara pheromone update
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


Ara pheromone decay
ARA Pheromone Decay

Pheromone is periodically decayed according to a decay rate, t


Ara loop prevention
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 hop

    • Packets identified based on source address and sequence number

  • The previous hop sets Pn,d = 0

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


Ara route recovery
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 hop

    • A new route request is issued if the data packet is returned to the source


Ara conclusion
ARA Conclusion

  • ARA is a MANET routing algorithm

  • Flooding is used to discover routes

  • Automatic retransmit used to recover from a route failure

    • Packet backtracking used if automatic retransmit fails

  • Next Hop probability proportional to pheromone on each link


Ara reference
ARA Reference

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


Si routing overview5
SI Routing Overview

  • Ant-Based Control

  • AntNet

  • Mobile Ants Based Routing

  • Ant Colony Based Routing Algorithm

  • Termite


Termite overview
Termite Overview

  • Termite is a MANET routing algorithm

  • Termite uses pheromone to produce next-hop probabilities

    • Random routing

  • Termite aims to reduce control traffic

  • Termite should scale across network size and volatility


Termite routing
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


Termite pheromone update
Termite Pheromone Update

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


Termite pheromone decay
Termite Pheromone Decay

Pheromone is periodically decayed according to a decay rate, t


Termite route recovery
Termite Route Recovery

If a transmission to a neighbor fails:

  • The neighbor is removed from the Pheromone Table

  • An alternative next-hop is calculated and the packet is resent

  • If no alternative exists, the packet is dropped


Termite route discovery rreq
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 pheromone

    • A route reply (RREP) is returned to the source


Termite route discovery rrep
Termite Route Discovery(RREP)

  • A route reply (RREP) packet follows the pheromone trail normally back to the RREQ source

    • The 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

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

  • Path discovery uses random walk

    • Route Discovery packets are unicast


Open issues
Open Issues

Termite still has many open questions

  • How to automatically determine routing parameters based on local information

    • Decay rate, t

    • 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 environment
Simulation Environment

  • 10 m transmission radius

  • 1 Mbps channel

  • 64B data packets

  • CBR source

    • 2 packets per second with acknowledgement





Termite reference
Termite Reference

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


Si advantages
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
More SI Advantages

  • Scalability

    • As with any colonies numbering in the millions, SI algorithms can potentially scale across several orders of magnitude

  • Distributed Algorithm

    • SI based algorithms are inherently distributed


Si disadvantages
SI Disadvantages

SI also suffers from:

  • Directional Links

    • Bidirectional links are generally assumed by using reverse paths

  • Novelty

    • SI is a relatively new approach to routing. It has not been characterized very well, analytically


Swarm intelligence conclusion1
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 probabilities

    • Critical packets may include specialized ant packets or all packets


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