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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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Journal ArticleDOI

hetDEEC: Heterogeneous DEEC protocol for prolonging lifetime in wireless sensor networks

TL;DR: A 3-level heterogeneous network model for WSNs to enhance the network lifetime and helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function.
Proceedings ArticleDOI

A Cluster-Based Algorithm for Energy-Efficient Routing in Wireless Sensor Networks

TL;DR: An energy-efficient routing algorithm based on cycle-switching cluster head that improves node energy efficiency, balances energy consumption of all sensor nodes, enhances reliability of data transmission and postpones network lifetime in comparison to LEACH is proposed.
Proceedings ArticleDOI

Quantifying load imbalance: A practical implementation for data collection in low power lossy networks

TL;DR: This paper proposes a greedy algorithm, that requires only partial topology knowledge, and works with the IETF standardized Routing Protocol for LLNs (RPL), without adding extra control overhead, which can work in highly varying link condition and large scale deployments.
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A MAC scheme for avoiding inter-cluster collisions in wireless sensor networks

TL;DR: This paper proposes a TDMA based MAC protocol for avoiding inter-cluster collisions which avoids collisions occurring among clusters.
Proceedings ArticleDOI

Anomaly based detection of Black Hole Attack on leach protocol in WSN

TL;DR: The effect of black hole on Low Energy Adaptive Clustering Hierarchy protocol researched by the dynamic authors is shown and an algorithm for the identification of the black hole attack is proposed.
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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Data Mining: Concepts and Techniques

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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