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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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Proceedings ArticleDOI

A Survey on Cluster Schemes in Ad Hoc Wireless Networks

TL;DR: Issues in energy efficient cluster schemes are considered: overlaps between the clusters; clusterhead (CH) not at the center of the cluster; higher power consumption in CHs than in normal nodes; energy wasted by duplicated transmission of data.
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Optimized Self Organized Sensor Networks

TL;DR: The experiments results illustrate that the proposed algorithm could result in clusters with smaller number of cluster heads than others with any density of sensor networks, but also that the performance is more stable, which is also verified through repeated experiments.
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LASA: Low-energy adaptive slot allocation scheduling algorithm for wireless sensor networks

TL;DR: This paper proposes LASA (Low-energy Adaptive Slot Allocation) to replace the fix slot size in classical Time Division Multiple Access schemes by a variable slot size that dynamically adapts to the data size generated at sensor nodes.
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Data clustering in wireless sensor network implemented on self organization feature map (SOFM) neural network

TL;DR: In this paper, a clustering technique is implemented in the sensor network by implementing self organizing feature map (SOFM) neural network, which reduces the battery consumption over the huge data management.
Journal ArticleDOI

EBDHR: Energy Balancing and Dynamic Hierarchical Routing algorithm for wireless sensor networks

TL;DR: The new hierarchical and dynamic routing algorithm is proposed to balance energy consumption among the nodes and to prevent from energy holing problem and Simulation results show that the proposed algorithm prolongs the network lifetime about 40% compared to the LEACH protocol.
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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TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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An application-specific protocol architecture for wireless microsensor networks

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