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Proceedings ArticleDOI

Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach

O. Younis, +1 more
- Vol. 1, pp 629-640
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TLDR
A protocol is presented, HEED (hybrid energy-efficient distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree, which outperforms weight-based clustering protocols in terms of several cluster characteristics.
Abstract
Prolonged network lifetime, scalability, and load balancing are important requirements for many ad-hoc sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new energy-efficient approach for clustering nodes in ad-hoc sensor networks. Based on this approach, we present a protocol, HEED (hybrid energy-efficient distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED does not make any assumptions about the distribution or density of nodes, or about node capabilities, e.g., location-awareness. The clustering process terminates in O(1) iterations, and does not depend on the network topology or size. The protocol incurs low overhead in terms of processing cycles and messages exchanged. It also achieves fairly uniform cluster head distribution across the network. A careful selection of the secondary clustering parameter can balance load among cluster heads. Our simulation results demonstrate that HEED outperforms weight-based clustering protocols in terms of several cluster characteristics. We also apply our approach to a simple application to demonstrate its effectiveness in prolonging the network lifetime and supporting data aggregation.

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Citations
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Journal ArticleDOI

A Sequential Sampling Framework for Spectral k-Means Based on Efficient Bootstrap Accuracy Estimations: Application to Distributed Clustering

TL;DR: A sequential sampling framework that iteratively enlarges the sample size until the k-means results become indistinguishable from the asymptotic (infinite-data) output is proposed, and it is demonstrated that the proposed framework can be generalized to handle spectral clustering.
Journal ArticleDOI

Game Theoretic Approach for Power Control using Error Control Coding in Wireless Sensor Networks

TL;DR: A power control solution for wireless sensor network (WSN) considering ECC in the analytical setting of a game theoretic approach is proposed; results show that the proposed algorithm employing ECC achieves the best response for the sensor nodes by consuming less power.
Book ChapterDOI

A Tree-Based Multiple-Hop Clustering Protocol for Wireless Sensor Networks

TL;DR: A static Tree-based Multiple-Hop Distributed Hierarchical Agglomerative Clustering approach for wireless sensor networks (WSNs) that adopts an energy-aware cluster-head election policy to balance the energy consumption and workload among sensor nodes in the network.

Challenges in maximizing the life of Wireless Sensor Network

TL;DR: This work focuses primarily on different protocols which represent the most suitable technique for energy saving in wireless sensor networks, and surveys in-network processing which can guarantee a significant amount of energy saving.
Proceedings ArticleDOI

(EDsHEED) Enhanced Simplified Hybrid, Energy-efficient, Distributed Clustering for Wireless Sensor Network

TL;DR: This research proposes protocol improvement of sHEED which is reducing intra-cluster communication cost by creating multi-level cluster routing protocol based on s HEED to reduce the energy consumption, which increase the network lifetime and also reduce energy consumption caused by multi hop communication as a result from larger area networks.
References
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TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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An application-specific protocol architecture for wireless microsensor networks

TL;DR: This work develops and analyzes low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality.
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