Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks

Author

GhasemAghaei, R. ; Rahman, Abdur ; Gueaieb, Wail ; El Saddik, Abdulmotaleb

Author_Institution

Univ. of Ottawa, Ottawa

fYear

2007

fDate

1-3 May 2007

Firstpage

1

Lastpage

6

Abstract

The field of routing and sensor networking is an important and challenging research area of network computing today. Advancements in sensor networks enable a wide range of environmental monitoring and object tracking applications. Routing in sensor networks is a difficult problem: as the size of the network increases, routing becomes more complex. Therefore, biologically-inspired intelligent algorithms are used to tackle this problem. Ant routing has shown excellent performance for sensor networks. In this paper, we present a biologically-inspired swarm intelligence-based routing algorithm, which is suitable for sensor networks. Our proposed ant routing algorithm also meet the enhanced sensor network requirements, including energy consumption, success rate, and time delay. The paper concludes with the measurement data we have found.

Keywords

learning (artificial intelligence); optimisation; telecommunication computing; telecommunication network routing; wireless sensor networks; ant colony-based reinforcement learning algorithm; ant routing algorithm; biologically-inspired intelligent algorithms; environmental monitoring; intelligence-based routing algorithm; network computing; networks routing; object tracking; wireless sensor networks; Biology computing; Biosensors; Computer networks; Intelligent networks; Intelligent sensors; Learning; Monitoring; Particle swarm optimization; Routing; Wireless sensor networks; ant routing; sensor network;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE

Conference_Location

Warsaw

ISSN

1091-5281

Print_ISBN

1-4244-0588-2

Type

conf

DOI

10.1109/IMTC.2007.379173

Filename

4258155