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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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Proceedings ArticleDOI

A QoS-based Adaptive Clustering Algorithm for Wireless Sensor Networks

TL;DR: A algorithm, QAC (QoS-based adaptive clustering algorithm), which not only concerns energy consumption but also can improve the reliability and the steadiness of wireless sensor networks by establishing a dual cluster-head model.
Journal ArticleDOI

Energy-Efficient and Fast Data Gathering Protocols for Indoor Wireless Sensor Networks

Abdullah Erdal Tümer, +1 more
- 27 Aug 2010 - 
TL;DR: Simulation results show that the proposed protocols are more energy-efficient than the conventional LEACH protocol.
Journal ArticleDOI

Designing a Wireless Sensor Network for Ocean Status Notification System

TL;DR: A novel scheme based on Wireless Sensor Networks for Ocean Status Notification System (WOSNS) so that the environmental conditions of the oceans can be monitored and is better than other protocols in terms of average delivery ratio, network lifetime and network traffic.
Proceedings ArticleDOI

Broadcasting for network lifetime maximization in wireless sensor networks

TL;DR: The experimental results demonstrate that LM-PB provides longer network lifetime compared with the existing passive protocols, and the total number of successfully routed messages, termed as the network capacity, is also maximized by LM- PB.
Journal ArticleDOI

In-network approximate computation of outliers with quality guarantees

TL;DR: This paper introduces an in-network outlier detection framework, based on locality sensitive hashing, extended with a novel boosting process as well as efficient load balancing and comparison pruning mechanisms that can reliably identify outlier readings using a fraction of the bandwidth and energy that would otherwise be required.
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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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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