Optimization algorithms for large self-structuring networks

Krishnan, Rajesh; Ramanathan, Ram; Steenstrup, Martha

doi:10.1109/infcom.1999.749254

Cited by 47 publications

(28 citation statements)

References 22 publications

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“…Ramanathan and Steenstrup [21], and Krishnan, Ramanathan, and Steenstrup [15] propose a clustering algorithm that controls the size of each cluster and the number of hierarchical levels. Their clustering approach follows the node ID approach.…”

Section: Related Workmentioning

confidence: 99%

ACE: An Emergent Algorithm for Highly Uniform Cluster Formation

Chan

Perrig

2004

Lecture Notes in Computer Science

274

194

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Abstract. The efficient subdivision of a sensor network into uniform, mostly non-overlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing. We present ACE, an algorithm that results in highly uniform cluster formation that can achieve a packing efficiency close to hexagonal close-packing. By using the self-organizing properties of three rounds of feedback between nodes, the algorithm induces the emergent formation of clusters that are an efficient cover of the network, with significantly less overlap than the clusters formed by existing algorithms. The algorithm is scale-independent -it completes in time proportional to the deployment density of the nodes regardless of the overall number of nodes in the network. ACE requires no knowledge of geographic location and requires only a small constant amount of communications overhead.

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Section: Related Workmentioning

confidence: 99%

ACE: An Emergent Algorithm for Highly Uniform Cluster Formation

Chan

Perrig

2004

Lecture Notes in Computer Science

274

194

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“…Bounding the cluster size is a requirement imposed by routing protocol complexity-the control message overhead decreases with cluster size up to the point that the large cluster size causes the intra-cluster control message overhead to dominate [13,14,22]. Furthermore, resource constraints or specific network architectures often impose limits on the cluster size.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…In Equation 14, we use a result from [29], and the symbol in Equation 15 denotes asymptotic equality as n → ∞. We note that our probabilistic guarantee that initiators do not interfere is robust and does not require to start all the timers at the same time.…”

Section: Timer Design Approachmentioning

confidence: 99%

Efficient clustering algorithms for self-organizing wireless sensor networks

Krishnan¹,

Starobinski

2006

Ad Hoc Networks

112

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Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local "growth budgets" to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time.However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.

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“…Krishnan et al [13] have explored different graph partitioning schemes for Internet-like graphs. Their target problem is, as a consequence, somewhat different from ours.…”

Section: Resultsmentioning

confidence: 99%

“…Similar clustering goals have been specified in related clustering work for wireless networks in [13], [14].…”

Section: Desired Goals Of the Clustering Schemementioning

confidence: 95%

A clustering scheme for hierarchical control in multi-hop wireless networks

Banerjee

Khuller

Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer An

428

297

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Abstract-In this paper we present a clustering scheme to create a hierarchical control structure for multi-hop wireless networks. A cluster is defined as a subset of vertices, whose induced graph is connected. In addition, a cluster is required to obey certain constraints that are useful for management and scalability of the hierarchy. All these constraints cannot be met simultaneously for general graphs, but we show how such a clustering can be obtained for wireless network topologies. Finally, we present an efficient distributed implementation of our clustering algorithm for a set of wireless nodes to create the set of desired clusters.

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Optimization algorithms for large self-structuring networks

Cited by 47 publications

References 22 publications

ACE: An Emergent Algorithm for Highly Uniform Cluster Formation

ACE: An Emergent Algorithm for Highly Uniform Cluster Formation

Efficient clustering algorithms for self-organizing wireless sensor networks

A clustering scheme for hierarchical control in multi-hop wireless networks

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