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
Resilient Cluster Formation for Sensor Networks
TL;DR: Three techniques for resilient cluster formation are proposed: the simple neighbor validation provides a simple yet effective way to validate a sensor's neighbors; the priority-based selection organizes clusters based on the sensor's priority of being a cluster head; and the centralized detection further enhances the security by detecting misbehaving nodes.
Journal ArticleDOI
Cluster overlay broadcast (COB): MANET routing with complexity polynomial in source-destination distance
TL;DR: Cluster Overlay Broadcast (COB), a low-complexity routing algorithm for MANETs, is developed and analyzed and it is formally proved that, if there exists a route from a source to a destination node with a minimum hop count of A, then COB discovers a route with at most O(/spl Delta/) hops.
Proceedings ArticleDOI
An Energy-Efficient Sensor Routing with low latency, scalability in Wireless Sensor Networks
Hayoung Oh,Kijoon Chae +1 more
TL;DR: This paper presents a sensor routing scheme, EESR (energy-efficient sensor routing) that provides energy-efficient data delivery from sensors to the base station and achieves significant energy savings and outperform idealized transitional schemes under the investigated scenarios.
Journal ArticleDOI
An energy-efficient QoS routing for wireless sensor networks using self-stabilizing algorithm
TL;DR: A self-stabilizing hop-constrained energy-efficient (SHE) protocol for constructing minimum energy networks for hard real-time routing and an adaptive routing protocol is proposed to convey aggregate data packets from CHs to BSs in different routes depending on their current AT values, thus meeting their QoS requirements while prolonging the network lifetime.
Journal ArticleDOI
Continuous K-Means Monitoring with Low Reporting Cost in Sensor Networks
TL;DR: This paper proposes the reading reporting tree, a hierarchical data collection, and analysis framework, and develops several reporting cost-effective methods using reading reporting trees in continuous k-means monitoring.
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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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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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