Distributed computing: a locality-sensitive approach
Distributed computing: a locality-sensitive approach
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
LEAP: efficient security mechanisms for large-scale distributed sensor networks
Proceedings of the 10th ACM conference on Computer and communications security
Multimedia content distribution in hybrid wireless networks using weighted clustering
Proceedings of the 2nd ACM international workshop on Wireless multimedia networking and performance modeling
Ad-hoc Networks: Fundamental Properties and Network Topologies
Ad-hoc Networks: Fundamental Properties and Network Topologies
PATM: priority-based adaptive topology management for efficient routing in ad hoc networks
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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Clustering algorithms form hierarchical structures of an otherwise homogeneous ad hoc or sensor network. Efficient clustering is a challenging task since the process should be completely self-organized and makes use of incomplete environmental information. As result of these requirements many clustering algorithms use local information and support 1-hop sized clusters. The contribution of this paper is to show how k-hop sized clusters can be formed and maintained by solely using local neighborhood information. k-hop sized clusters are clusters where nodes are at most k-hops away from the clusterhead, i.e. with a cluster diameter of at most 2k. The presented k-hop clustering algorithm takes advantage of nodes' mobility in order to form and maintain k-hop sized clusters. The paper concludes that mobility is a key force for supporting self-organization of clusters. The algorithm described works locally, asynchronously, and fully distributed.