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Sensors 2016, 16(1), 65;

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks

Department of Mechanical and Manufacturing Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Author to whom correspondence should be addressed.
Academic Editor: Luis Javier Garcia Villalba
Received: 23 October 2015 / Revised: 28 December 2015 / Accepted: 29 December 2015 / Published: 6 January 2016
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency. View Full-Text
Keywords: wireless sensor networks; clustering; distributed adaptive systems; reconfigurability wireless sensor networks; clustering; distributed adaptive systems; reconfigurability

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Gholami, M.; Brennan, R.W. A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks. Sensors 2016, 16, 65.

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