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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

CAT: The New Clustering Algorithm Based on Two-Tier Network Topology for Energy Balancing in Wireless Sensor Networks

TL;DR: The new clustering algorithm based on two-tier network topology namely CAT, which selects a best sensor node as a cluster head in two phases by different methods and prolongs the network lifetime about 45% and 19% compared to the LEACH and HEED, respectively.
Journal ArticleDOI

A Qos-Aware, Hybrid Particle Swarm Optimization-Cuckoo Search Clustering Based Multipath Routing in Wireless Sensor Networks

TL;DR: A QoS-aware, multipath routing protocol in which sensor nodes are clustered using the hybrid Particle Swarm Optimization-Cuckoo Search Optimization algorithm that outperforms current protocols in terms of QoS parameters such as throughput, packet delivery ratio, end-to-end delay, and network lifetime, according to simulation results.
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Design and analysis of mobility-aware clustering algorithms for wireless mesh networks

TL;DR: This work identifies the cases where clustering is helpful and proposes two clustering schemes that take into consideration the mobility properties of the users in order to improve the WMN performance and proves that both schemes can achieve significant gains in terms of radio resource utilization.
Proceedings ArticleDOI

A Novel Energy-Efficient Routing Algorithm in Multi-sink Wireless Sensor Networks

TL;DR: Simulation results show that compared with other clustering algorithms, LEBDPC algorithm can effectively reduce energy consumption and prolong network lifetime.
Proceedings ArticleDOI

RF signal Strength based clustering protocols for a self-organizing cognitive radio network

TL;DR: Two novel distributed clustering algorithms that exploit cognitive radio based principles in that they have the ability to learn from received signal strength indicator (RSSI) beacons, to form clusters which reduce the average distance between nodes and cluster head, as well as reducing the level of overlap between clusters.
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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