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
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
Real-time forest fire detection with wireless sensor networks
TL;DR: The wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach and a neural network method is applied to in-network data processing.
Journal ArticleDOI
Clustering in sensor networks
TL;DR: A state-of-the-art and comprehensive survey on clustering approaches in WSNs, which surveys the proposed approaches in the past few years in a classified manner and compares them based on different metrics such as mobility, cluster count, cluster size, and algorithm complexity.
Proceedings ArticleDOI
Using mobile relays to prolong the lifetime of wireless sensor networks
TL;DR: This paper investigates the benefits of a heterogeneous architecture for wireless sensor networks composed of a few resource rich mobile nodes and a large number of simple static nodes and finds that using the mobile node as a sink results in the maximum improvement in lifetime.
Wireless Sensor Networks and Applications: a Survey
Carlos F. García-Hernández,Pablo H. Ibargüengoytia-González,Joaquín García-Hernández,Jesus Arturo Perez-Diaz +3 more
TL;DR: A survey on Wireless Sensor Networks (WSN) and their technologies, standards and applications was carried out, finding many new and exciting application areas for remote sensing.
Journal ArticleDOI
Cluster head election techniques for coverage preservation in wireless sensor networks
Stanislava Soro,Wendi Heinzelman +1 more
TL;DR: This paper takes a unique look at the cluster head election problem, specifically concentrating on applications where the maintenance of full network coverage is the main requirement and uses coverage-aware selection of cluster head nodes, active sensor nodes and routers.
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.
Book
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.