Hierarchically-organized, multihop mobile wireless networks for quality-of-service support
Mobile Networks and Applications - Special issue: mobile multimedia communications
On network-aware clustering of Web clients
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
K-clustering in wireless ad hoc networks
Proceedings of the second ACM international workshop on Principles of mobile computing
WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
Clustering Hosts in P2P and Global Computing Platforms
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Connectivity-Based k-Hop Clustering in Wireless Networks
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Distributed Clustering for Ad Hoc Networks
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
Size-restricted cluster formation and cluster maintenance technique for mobile ad hoc networks
International Journal of Network Management
Scalable versus Accurate Physical Layer Modeling in Wireless Network Simulations
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Self-organization in communication networks: principles and design paradigms
IEEE Communications Magazine
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
Node clustering in wireless sensor networks: recent developments and deployment challenges
IEEE Network: The Magazine of Global Internetworking
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An efficient way to bound the size of clusters in large-scale self-organizing wireless networks is to rely on a budget-based strategy. The side effect of conventional budget-based clustering approaches is that they generate a potentially large number of small, even single-node, clusters. The consequence is that while clusters are bounded, their average size may be far from the expected value (the budget), which negatively impacts the performance of the communication systems running on top of it. In contrast, we propose Fair and Flexible Budget-Based Clustering (FFBC) to form size-controlled clusters in large-scale self-organizing networks. For a given target cluster size, our approach outperforms previous budget-based algorithms by creating clusters of average size closer to the requested value and avoiding isolated nodes.