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 Stigmergy Indirect communication Generally through the environment

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 The food foraging behavior of ants exhibits swarm intelligence

Principles of Swarm Intelligence What makes a Swarm Intelligent system work? Positive Feedback Negative Feedback Randomness Multiple 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 Decay Distant 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 random Exploration 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 agents Agents may be simple Behaviors 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 Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

SI Routing Overview Ant-Based Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

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 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 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 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 Update  p determined by age of packet Probabilities remain normalized

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 Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

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 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) 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 packets Forward Ants experience the same congestion and delay as data Backward 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 Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

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 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 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 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 network SPF 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 Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

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

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 Pheromone is periodically decayed according to a decay rate, 

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

SI Routing Overview Ant-Based Control AntNet Mobile Ants Based Routing Ant Colony Based Routing Algorithm Termite

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

Termite Pheromone Decay Pheromone is periodically decayed according to a decay rate, 

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) 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) 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 minimizes control traffic by allowing all packets to explore the network Path discovery uses random walk Route 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 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 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 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