Artificial insect algorithms for routing in wireless sensor systems

Li-Minn Ang; A M Zungeru; K P Seng; D Habibi

doi:10.4018/978-1-4666-6328-2.ch009

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Artificial insect algorithms for routing in wireless sensor systems

Book chapter

Peer reviewed

Artificial insect algorithms for routing in wireless sensor systems

Li-Minn Ang, A M Zungeru, K P Seng and D Habibi

Emerging Research on Swarm Intelligence and Algorithm Optimization, pp.191-213

IGI Global

2015

DOI: https://doi.org/10.4018/978-1-4666-6328-2.ch009

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Abstract

Energy utilization

Network routing

Routing algorithms

Autocatalytic

Best effort services

Global objective

Network density

Network energy consumption

Routing problems

Social insects

Wireless sensor system

Wireless sensor networks

Social insect communities are formed from simple, autonomous, and cooperative organisms that are interdependent for their survival. These communities are able to effectively coordinate themselves to achieve global objectives despite a lack of centralized planning. This chapter presents a study of artificial insect algorithms for routing in wireless sensor networks, with a specific focus on simulating termites and their behaviour in their colony. The simulating behaviour demonstrates how the termites make us of an autocatalytic behaviour in order to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-Hill demonstrates the principle of the termite behaviour for solving the routing problem in wireless sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results were compared with other routing algorithms with varying network density and showed that the proposed algorithm is scalable and improved on network energy consumption with a control over best-effort service.

Details

Title: Artificial insect algorithms for routing in wireless sensor systems
Authors: Li-Minn Ang (Author) - Edith Cowan University
A M Zungeru (Author) - Federal University Oye Ekiti
K P Seng (Author) - Edith Cowan University
D Habibi (Author) - Edith Cowan University
Contributors: Yuhui Shi (Editor) - Southern University of Science and Technology
Publication details: Emerging Research on Swarm Intelligence and Algorithm Optimization, pp.191-213
Publisher: IGI Global
Date published: 2015
DOI: 10.4018/978-1-4666-6328-2.ch009; 10.4018/978-1-4666-6328-2
ISBN: 9781466663282
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513894102621
Output Type: Book chapter

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Collaboration types: International collaboration
Web Of Science research areas: Computer Science, Artificial Intelligence