Self-stabilization
The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
Self-stabilization over unreliable communication media
Distributed Computing - Special issue: Self-stabilization
Secure Distributed Cluster Formation in Wireless Sensor Networks
ACSAC '06 Proceedings of the 22nd Annual Computer Security Applications Conference
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Construction algorithms for k-connected m-dominating sets in wireless sensor networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Empire of colonies: Self-stabilizing and self-organizing distributed algorithm
Theoretical Computer Science
Robust self-stabilizing weight-based clustering algorithm
Theoretical Computer Science
Construction K-Dominating Set with Multiple Relaying Technique in Wireless Mobile Ad Hoc Networks
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02
A Self-stabilizing K-Clustering Algorithm Using an Arbitrary Metric
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
A Self-stabilizing (k,r)-clustering Algorithm with Multiple Paths for Wireless Ad-hoc Networks
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Distributed k-clustering algorithms for random wireless multihop networks
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
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Wireless Ad-hoc networks are distributed systems that often reside in error-prone environments. Self-stabilization lets the system recover autonomously from an arbitrary system state, making the system recover from errors and temporarily broken assumptions. Clustering nodes within ad-hoc networks can help forming backbones, facilitating routing, improving scaling, aggregating information, saving power and much more. We present a self-stabilizing distributed (k,r)-clustering algorithm. A (k,r)-clustering assigns k cluster heads within r communication hops for all nodes in the network while trying to minimize the total number of cluster heads. The algorithm assumes a bound on clock frequency differences and a limited guarantee on message delivery. It uses multiple paths to different cluster heads for improved security, availability and fault tolerance. The algorithm assigns, when possible, at least k cluster heads to each node within O(rπλ3) time from an arbitrary system configuration, where π is a limit on message loss and λ is a limit on pulse rate differences. The set of cluster heads stabilizes, with high probability, to a local minimum within O(rπλ4glogn) time, where n is the size of the network and g is an upper bound on the number of nodes within 2r hops.