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From Biology to Technology: The Ant Routing Algorithm for Mobile Ad-hoc Netorks

Mesut Güneş, Petri Mähönen— 2004

A mobile ad-hoc network (MANET) is a collection of mobile nodes which communicate over radio. These networks have an important advantage, they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. This flexibility, however, comes at a price: communication is dif cult to organize due to frequent topology changes. The Ant-Colony-Based Routing Algorithm (ARA) is highly adaptive, efficient and scalable. It is based on ant algorithms which are a class of swarm intelligence. Ant algorithms try to map the solution capability of ant colonies to mathematical problems. In this paper we present some extensions to the basic idea and show through simulation results the performance gain and compare it with AODV and DSR. Furthermore, we discuss the extensibility of the approach.

TitelFrom Biology to Technology: The Ant Routing Algorithm for Mobile Ad-hoc Netorks
VerfasserMesut Güneş, Petri Mähönen
VerlagProceedings of WWRF8bis Meeting of the Wireless World Research Forum, February 2004
Datum200402
ArtText
BibTeX Code@inproceedings{Guenes+:2004a, author = {Mesut G{\"u}nes and Petri M{\"a}h{\"o}nen}, title = {{From Biology to Technology: The Ant Routing Algorithm for Mobile Ad-hoc Netorks}}, booktitle = {Proceedings of WWRF8bis Meeting of the Wireless World Research Forum}, year = {2004}, address = {Beijing, China}, month = {February}, abstract = {A mobile ad-hoc network (MANET) is a collection of mobile nodes which communicate over radio. These networks have an important advantage, they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. This flexibility, however, comes at a price: communication is dif cult to organize due to frequent topology changes. The Ant-Colony-Based Routing Algorithm (ARA) is highly adaptive, efficient and scalable. It is based on ant algorithms which are a class of swarm intelligence. Ant algorithms try to map the solution capability of ant colonies to mathematical problems. In this paper we present some extensions to the basic idea and show through simulation results the performance gain and compare it with AODV and DSR. Furthermore, we discuss the extensibility of the approach.}, owner = {guenes}, timestamp = {2007.08.23} }