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Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

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Abstract

Wireless multimedia sensor networks (WMSN), with self-organizing and high fault tolerant characteristics, have achieved great advantages in target tracking region. However, the capabilities of these tiny devices are limited by their battery power, storage capacity, computational ability and communication bandwidth. In this paper, hybrid wireless multimedia sensors networks composed of acoustic and image sensors are proposed for target tracking. When the target appears in the detection area, it may change the environment parameters nearby, so acoustic sensors are used to gather target signal firstly. Then, a target location method is executed based on the strength of the received acoustic signal. Furthermore, to achieve energy-efficient target tracking with high reliability and robust, image sensors are used as supplements to the acoustic sensors. This approach also reduces the power consumption communication burden of the whole networks. In order to decrease the number of active nodes, Gauss Markov mobility model is also adopted to predict the target trajectory and minimize the tracking region with considering of vehicular kinematics. Simulation results verify that, compared with other algorithms, our scheme can reduce the energy consumption and improve tracking accuracy.

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Acknowledgements

Foundation item: The Fundamental Research Funds for the Central Universities (2015XKMS087).

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Authors and Affiliations

  1. School of Computer Sciences and Technology, China University of Mining & Technology, Xuzhou, 221116, China

    Shuo Xiao, Wei Li, Haifeng Jiang, Zhiou Xu & Zihao Hu

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  1. Shuo Xiao
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  2. Wei Li
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  3. Haifeng Jiang
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  4. Zhiou Xu
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  5. Zihao Hu
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Correspondence to Zhiou Xu.

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Xiao, S., Li, W., Jiang, H. et al. Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks. Multimed Tools Appl 77, 12003–12022 (2018). https://doi.org/10.1007/s11042-017-4846-z

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  • DOI: https://doi.org/10.1007/s11042-017-4846-z

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