Multicluster, mobile, multimedia radio network
Wireless Networks
WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
A Distributed Weighted Clustering Algorithm for Mobile Ad Hoc Networks
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
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Mobile Ad hoc networks (MANETs) are self-organizing and self-configuring multi-hop wireless networks without any pre-existing communication infrastructures or centralized management. Scalability in MANETs is a new issue where network topology includes large number of nodes and demands a large number of packets in limited wireless bandwidth and nodes mobility that results in a high frequency of failure regarding wireless links. Clustering in MANETs is an important topic that divides the large network into several sub networks and widely used in efficient network management, improving resource management, hierarchical routing protocol design, Quality of Service and a good monitoring architecture of MANETs security. Subsequently, many clustering approaches have been proposed to divide nodes into clusters to support routing and network management. In this paper, we propose a new efficient weight based clustering algorithm. It takes into consideration the metrics: trust (T), density (D), Mobility (M) and energy (E) to choose locally the optimal cluster heads during cluster formation phase. In our proposed algorithm each cluster is supervised by its cluster head in order to ensure an acceptable level of security. It aims to improve the usage of scarce resources such as bandwidth, maintaining stable clusters structure with a lowest number of clusters formed, decreasing the total overhead during cluster formation and maintenance, maximizing lifespan of mobile nodes in the network and reduces energy consumption. Preliminary simulation experiments are conducted to compare the performance of our algorithm to Lowest ID, Highest Degree and WCA in terms of Average Number of CHs, Average Number of CH Changes, Total Number of Re-affiliations, Clusters Stability and Total Overhead. The initial results show that our scheme performs better than other clustering schemes based on the performance metrics considered.