Ants based routing
This presentation is the property of its rightful owner.
Sponsored Links
1 / 15

Ants-based Routing PowerPoint PPT Presentation


  • 95 Views
  • Uploaded on
  • Presentation posted in: General

Ants-based Routing. Marc Heissenb ü ttel University of Berne http://www.iam.unibe.ch/. 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.

Download Presentation

Ants-based Routing

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Ants based routing

Ants-based Routing

Marc Heissenbüttel

University of Berne

http://www.iam.unibe.ch/

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


Introduction

Introduction

  • 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


Ants finding the shortest path

Ants finding the shortest Path

Food Source

Nest

?


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

2

1

3

4

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

A

Node 1

Dest.


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

A2,1

A1,1

A1,8

A2,2

A1,2

N1

K

N2

N4

N3

...

A2,3

...


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

S

D


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

Summary

Open Issues


  • Login