Tiered authentication of multicast traffic in wireless ad-hoc networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Modelling and analysis of strategies in the design of WSAN coordination systems
Journal of Parallel and Distributed Computing
Exploiting architectural techniques for boosting base-station anonymity in wireless sensor networks
International Journal of Sensor Networks
An energy-efficient adaptive clustering algorithm with load balancing for wireless sensor network
International Journal of Sensor Networks
Spatially correlated multi-modal wireless sensor networks: a coalitional game theoretic approach
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Networked computing in wireless sensor networks for structural health monitoring
IEEE/ACM Transactions on Networking (TON)
ACM Transactions on Sensor Networks (TOSN)
Wireless Personal Communications: An International Journal
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Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments, we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allow distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.