Proceedings ArticleDOI
Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach
O. Younis,Sonia Fahmy +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.read more
Citations
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Proceedings ArticleDOI
Analysis of Energy Consumption in Direct Transmission and Multi-hop Transmission for Wireless Sensor Networks
TL;DR: An optimal hop number is deduced for minimizing the energy consumption during the multi-hop transmission and an energy efficient routing scenario is presented with diagram so as to illustrate how the network lifetime can be prolonged.
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Improving network systems performance by clustering distributed database sites
TL;DR: This work presents a novel algorithm for clustering distributed database network sites based on the communication time as database query processing is time dependent, and shows that a better network distribution is achieved with significant network servers load balance and network delay.
HCTE: Hierarchical Clustering based routing algorithm with applying the Two cluster heads in each cluster for Energy balancing in WSN
TL;DR: This work proposes the new clustering based routing protocol namely HCTE that cluster head selection mechanism in it is done in two separate stages so there will be two cluster head in a cluster and the routing algorithm used in proposed protocol is multi hop.
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On the optimal randomized clustering in distributed sensor networks
TL;DR: This paper first demonstrates that the general problem of optimal clustering with arbitrary cluster-head selection is NP-hard, and focuses on randomized clustering in which sensor nodes form clusters in a distributed manner using a probabilistic cluster- head selection process.
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Energy-Efficient Strategies for Cooperative Multichannel MAC Protocols
TL;DR: This paper proposes two energy-efficient strategies: in-situ energy conscious DISH, which uses existing nodes only, and altruistic DISD, which requires additional nodes called altruists to be the right choice in general.
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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.
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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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