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Ants-based Routing. Marc Heissenb ü ttel University of Berne Berne, 2002-04-03. Table of Contents. Introduction Meta-heuristic Ant Based Control (ABC) AntNet Ants-based Routing in Mobile Ad Hoc Networks Conclusion and Open Issues. Introduction.

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ants based routing

Ants-based Routing

Marc Heissenbüttel

University of Berne

Berne, 2002-04-03

table of contents
Table of Contents
  • Introduction
  • Meta-heuristic
  • Ant Based Control (ABC)
  • AntNet
  • Ants-based Routing in Mobile Ad Hoc Networks
  • Conclusion and Open Issues
  • Nature as a model for computer scientists
  • Social insects
    • simple behavior by limited individuals
    • but together can solve complex tasks
  • “Finding shortest path” applied to “Traveling Salesman Problem”, routing in networks,...
  • Stigmergy ~ “Communication through the environment”
    • Characterized by
      • modification of physical environment
      • local nature of released information
    • Lay down trail of pheromones, follow trail with certain probability
artificial ants
Artificial Ants
  • Adapt the ants foraging behavior to artificial multi-agent system
    • choosing appropriate state variables
    • only local access to these variables for artificial ants
  • Similarities
    • population of concurrent and asynchronous entities
    • real ants deposit pheromone, artificial ants change numeric information
    • stochastic decision policy
  • Differences
    • internal state
    • amount of pheromone is a function of the quality of the solution
    • timing in pheromone laying
    • extra capabilities (e.g. look ahead, local optimization)
foraging ant meta heuristic
“Foraging ant” meta-heuristic
  • initialize_ant ()
  • while (current_state  target_state)
    • A = read_local_pheromone-table()
    • P = compute_transition_probabilities (A, M, problem_constraints)
    • next_state = apply_ant_decision_policy (P, problem_constraints)
    • move_to_next_state (next_state)
    • if (step-by-step_pheromone_update)
      • update_pheromone_table() // deposit pheromone on visited arc
    • update_ant_memory()
  • if (delayed_pheromone_update)
    • evaluate_solution()
    • update_pheromone_tables() // deposit pheromone on ALL visited arcs
  • die()
ant based control abc i





Ant-Based Control (ABC) I
  • Pheromone tables
    • each table has an entry for every destination and neighbour
    • Pheromone laying ~ “updating routing table”
    • updating row for Dest. s when arriving from source node s over i in j
    • ants are launched from any node to any other node (random)
    • cost-symmetrical links required
ant based control abc ii
Ant-Based Control (ABC) II
  • Find short routes, but avoid heavily congested nodes
    • delay ants at congested nodes
    • make pheromone increase dependent of ant’s age
  • Calls operate independently of ants
    • deterministic (always neighbour with highest probability is chosen)
  • Noise
    • to overcome blocking and shortcut problem
    • Noise factor f
      • ant choosing path purely random with prob. f
      • with prob. (1-f) choosing path according to pheromone tables
antnet i
AntNet I

Node 3

  • Packet-switched network routing
    • cost-asymmetrical links
  • Three kind of packets
    • forward ant
    • backward ant
    • data
  • Two data structures at node k
    • routing table Tk
    • array as model of traffic distribution

Node 2


Node 1


antnet ii algorithm
AntNet II (Algorithm)
  • forward ant Fs->d launched with d according to traffic patterns
  • forward ant keeps track of path and traffic conditions
    • cycle detection possible
  • selection of next hop as function of Tk and queues of the links ln
    • system more reactive
  • At destination d, forward ant Fs->d generates backward ant Bd->s and transfers all its memory to it
  • Backward ant same path in reverse direction
    • not in same link queues as data packets and forward ants
  • Updating the two data structures Tk and Mk for destination d
  • At source s, backward ant dies
antnet iii differences to abc
AntNet III (Differences to ABC)
  • Real trip times
  • Additional data structure
  • Pheromone is deposited delayed
    • backward ant
    • cost-asymmetrical links
  • Use of local heuristics
  • Data packets are routed probabilistic
    • multipath routing
ants based routing in mobile ad hoc networks
Ants-based Routing in Mobile Ad Hoc Networks
  • Not possible to save all nodes in routing table
    • node K only maintains entries for areas Ai,j and neighboring nodes Nk
  • Areas of different size
  • One pheromone trail per area
  • Evaporation is a function of node’s velocity
  • Ants are sent to random coordinates within area














ants based routing in mobile ad hoc networks1
Ants-based Routing in Mobile Ad Hoc Networks
  • Link costs asymmetrical, no backward ant possible
    • Link costs transferred from neighbouring nodes by data packets moving in opposite direction
  • Not possible to use pheromone trails for ants (only data packets)
    • Traffic as a approximation for pheromone trails



ants based routing in mobile ad hoc networks2
Ants-based Routing in Mobile Ad Hoc Networks
  • Node only needs knowledge of its position and the destination
  • Ants keep track of intermediate nodes coordinates
    • recursively merge coordinates
  • Updating not only node at destination, but also others
  • Evaporation, Size of the Cell, ... as a function of the velocity
  • Associativity as link cost factor
summary and open issues
Outperformed a lot of today’s used algorithms in a variety of problems

High level of redundancy and fault tolerance

Multipath routing

Well suited for dynamic, decent-ralized problems

Little routing overhead


Deals efficiently with topology changes?

Scalability in terms of # of nodes and geographical coverage area?

Battery utilization -> sleep mode

Asymmetrical links

Combination with existing schemes

proactive, reactive


Summary and Open Issues


Open Issues