Max-min d-cluster formation in wireless ad hoc networks

By Alan D. Amis, Ravi Prakash, D. Huynh, T. Vuong. Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), 2000

1,103 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/d13033820a66997b91734739f9809125d971269f

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Citations in the corpus

Node clustering in wireless sensor networks: recent developments and deployment challenges (2006, background): “Another class favors nodes with larger degrees (e.g., Kuhn et al. [5] and Amis et al. [6]) in order to create dense clusters and elect the minimal dominating set of CHs (the degree of a node is the number of its neighbors within a prespecified transmision called the cluster range).”
The Emergence of Networking Abstractions and Techniques in TinyOS (2004, background): “One example is distributed cluster formation, about which there has been a large amount of publication in the ad-hoc and sensor network communities [12, 3, 5].”
An extended localized algorithm for connected dominating set formation in ad hoc wireless networks (2004, background): “In early schemes [3], [15], every nonclusterhead that has a neighbor in another cluster is designated as a connector, which results in a larger CDS.”
Topology management in ad hoc networks (2003, methodology): “Lowest ID [ 1 , 3, 11, 13, 19]: In this approach, the node identier is used to elect MDS members.”
ACE: An Emergent Algorithm for Highly Uniform Cluster Formation (2004, background): “Examples of node ID based clustering protocols include [1, 2, 5, 9, 17].”
Topology control for wireless sensor networks (2003, background): “Due to the flow concentration, scalability can become an obstacle [6] for a large number of sensors, and techniques to introduce heterogeneity [46], hierarchy [45], clustering [16, 26], localization [41], and locationawareness [39] are developed.”
Connectivity Based k-Hop Clustering in Wireless Networks (2002, methodology): “A variant of klowestID scheme was proposed independently in [APVH], which also shows that minimum d-hop dominating set problem is NP-complete.”
Geometric spanners for wireless ad hoc networks (2002, background): “On the other hand, the hierarchical structures used typically are based on dominating set or connected dominating set, or their extensions such as d-dominating set [15].”
Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach (2004, background): “5) Cluster heads are well-distributed over the sensor ﬁeld.”
A survey on clustering algorithms for wireless sensor networks (2007, neutral): “K-hop clustering is K-dominat-ing set problem [41–43].”
Power control and clustering in ad hoc networks (2003, background): “Some algorithmic aspects of clustering are analyzed in [22] and [23].”
HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks (2004, background): “The approach in [20] selects a d-hop dominating set in OðdÞ time to cluster the network based on node ID, while the approach in [45] selects a dominating set in constant time using linear programming relaxation techniques.”
Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks (2005, methodology): “Many clustering algorithms have been proposed [ 3-20 ].”
Clustering in sensor networks: A literature survey (2014, methodology): “Extensive simulations provided in the paper indicate that CAWT outperforms Max–Min heuristic (Amis et al., 2000) algorithm, in terms of time complexity and cluster formation.”
Online power-aware routing in wireless Ad-hoc networks (2001, methodology): “To improve the scalability of routing algorithms for large networks, many hierarchical routing methods have been proposed in [20, 8, 22, 2, 11, 28].”
Load-balancing clusters in wireless ad hoc networks (2000, methodology): “The Max-Min heuristic [ 12 ] was developed to extend the notion of -hop clusters and generalizes cluster formation to -hop clusters.”