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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
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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Sleep/wake scheduling for multi-hop sensor networks: Non-convexity and approximation algorithm

TL;DR: This work proposes an optimal sleep/wake scheduling algorithm, which satisfies a given message capture probability threshold with minimum energy consumption, and investigates the unique structure of the problem and achieves a solution that provably achieves at least 0.73 of the optimal performance.
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Performance analysis of clustering protocols in WSN

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Omnibus outlier detection in sensor networks using windowed locality sensitive hashing

TL;DR: This work introduces an omnibus outlier detection solution over spatiotemporally referenced sensor data that is capable of directly trading communication reduction for outlier Detection quality with predictable accuracy guarantees.
Proceedings ArticleDOI

EMEEDP: Enhanced Multi-hop Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network

TL;DR: In this article, an enhanced energy efficient distributed protocol for heterogeneous WSN has been reported, where an algorithm is proposed in the form of flow chart and based on various clustering equation proved that the proposed work accomplishes longer lifetime with improved QOS parameters.
Proceedings ArticleDOI

An Improvement of GAF for Lifetime Elongation in Wireless Sensor Networks

TL;DR: A novel topology control algorithm based on GAF is proposed: its main idea is to find the optimum position of the cluster head with a grid for energy saving, and divide the virtual grid dynamically and periodically, as well as take residual energy of each node into account, for uniform energy distribution.
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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