Bio-inspired adaptive routing in self-organized networks: A survey

Author

Xu Zhang ; Yanling Zhang ; Yang Li ; Zhongshan Zhang ; Keping Long

Author_Institution

Beijing Eng. & Technol. Center for Convergence Networks & Ubiquitous Services, Univ. of Sci. & Technol. Beijing (USTB), Beijing, China

fYear

2013

fDate

14-16 Aug. 2013

Firstpage

505

Lastpage

510

Abstract

Since the study of collective behavior of social species can help humans manage complex systems, bio-inspired algorithms will consequently improve the routing performance in self-organized networks. A plethora of studies on adaptive routing in self-organized networks has already been carried out. In this paper, adaptive routing as one of important self-organization issues is surveyed. Several well-known bio-inspired adaptive routing algorithms, such as Ant Colony Optimization (ACO), AntNet, AntHocNet, BeeHive, Multiple Ant Colony Optimization (MACO), Multiple Ant-Bee Colony (MABC), as well as their merits and demerits are analyzed in detail. The remaining challenges to face in adaptive routing in the future are also discussed in this paper.

Keywords

ant colony optimisation; telecommunication network routing; AntHocNet; AntNet; BeeHive; bio-inspired adaptive routing algorithms; bio-inspired algorithms; complex systems; multiple ant colony optimization; multiple ant-bee colony; routing performance; self-organized networks; social species; Adaptive systems; Algorithm design and analysis; Convergence; Delays; Load management; Optimization; Routing;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on

Conference_Location

Guilin

Type

conf

DOI

10.1109/ChinaCom.2013.6694648

Filename

6694648