A load-balancing and energy-aware clustering algorithm in wireless ad-hoc networks

Authors:
Wang Jin;Shu Lei;Jinsung Cho;Young-Koo Lee;Sungyoung Lee;Yonil Zhong
Affiliations:
Department of Computer Engineering, Kyung Hee University, Korea;Department of Computer Engineering, Kyung Hee University, Korea;Department of Computer Engineering, Kyung Hee University, Korea;Department of Computer Engineering, Kyung Hee University, Korea;Department of Computer Engineering, Kyung Hee University, Korea;Department of Computer Engineering, Kyung Hee University, Korea
Venue:
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Year:
2005

Citing 2
Cited 2

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Wireless Networks
Distributed Clustering for Ad Hoc Networks

ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks

Load balancer for energy efficient clustering protocol in MANETs

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Improved network survivability using multi-threshold adaptive range clustering M-TRAC algorithm for energy balancing in wireless sensor networks

Journal of High Speed Networks

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Abstract

Wireless ad-hoc network is a collection of wireless mobile nodes dynamically forming a temporary communication network without the use of any existing infrastructure or centralized administration. It is characterized by both highly dynamic network topology and limited energy. So, the efficiency of MANET depends not only on its control protocol, but also on its topology and energy management. Clustering strategy can improve the performance of flexibility and scalability in the network. With the aid of graph theory, genetic algorithm and simulated annealing hybrid optimization algorithm, this paper proposes a new clustering strategy to perform topology management and energy conservation. Performance comparison is made between the original algorithms and our two new algorithms, namely an improved weighting clustering algorithm and a novel Genetic Annealing based Clustering Algorithm (GACA), in the aspects of average cluster number, topology stability, load-balancing and network lifetime. The experimental results show that our clustering algorithms have a better performance on average.