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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
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
Ant-Based ControlAntNetMobile Ants Based RoutingAnt Colony Based Routing AlgorithmTermite
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
Ant-Based ControlAntNetMobile Ants Based RoutingAnt Colony Based Routing AlgorithmTermite
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-Based ControlAntNetMobile Ants Based RoutingAnt Colony Based Routing AlgorithmTermite
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
Ant-Based ControlAntNetMobile Ants Based RoutingAnt Colony Based Routing AlgorithmTermite
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,
,,
)(
)(
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