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A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks

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Abstract

Target tracking is an important application of sensor networks, particularly interesting for ecology applications related to wildlife monitoring. In this context, understanding the territorial occupation of animals is fundamental for understanding their habits. In this work, we propose the PRATIQUE—a prediction-based clustering algorithm for tracking targets considering a discrete sensor field divided into cells. This approach is based on two hierarchical levels: static clusters at the first level and dynamic clusters at the second level. This hybrid scheme reduces the cost of communication and ensures that all data generated by an event be delivered to a single node. We use Kalman, Alpha-Beta, or Particle Filters in order to predict the target’s position. Prediction is used to prepare the set of nodes that will detect the next event, thereby reducing the message overhead during the tracking task. Results show that prediction errors are close to one cell.

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Authors and Affiliations

  1. Federal University of Amazonas – UFAM, Manaus, Brazil

    Éfren L. Souza & Eduardo F. Nakamura

  2. University of Ontario Institute of Technology – UOIT, Oshawa, Canada

    Éfren L. Souza & Richard W. Pazzi

  3. Federal University of Western Para – UFOPA, Santarém, Brazil

    Éfren L. Souza

  4. Analysis, Research and Technological Innovation Center Foundation – FUCAPI, Manaus, Brazil

    Eduardo F. Nakamura

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  1. Éfren L. Souza
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Correspondence to Éfren L. Souza.

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Souza, É.L., Pazzi, R.W. & Nakamura, E.F. A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks. Wireless Netw 21, 2263–2278 (2015). https://doi.org/10.1007/s11276-015-0914-3

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