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Diffusion Distance-Based Predictive Tracking for Continuous Objects in Industrial Wireless Sensor Networks

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Abstract

In an industrial production process, the leakage of continuous objects poses a serious threat to production safety. In this paper, a diffusion distance-based predictive tracking algorithm is proposed for industrial wireless sensor networks (IWSNs), aiming to timely track the boundary of a continuous object after the occurrence of a leak. Based on the assumption that the motion of the continuous object follows an appropriate diffusion model, sensor nodes are able to capture environmental parameters for establishing the mathematical expression of the model locally. Through building up the relation of diffusion radius with time, each node predicts diffusion scope of the continuous object at different times and makes a judgment about whether it is suitable to be a boundary node. Moreover, to achieve high energy-efficiency, a sleep/wake cycle is introduced to involve a small number of nodes in the process of tracking, while the rest of nodes stay idle until an object approaches. Finally, a cluster-based competitive mechanism is proposed for reporting the location of boundary nodes. Simulation results demonstrated that our proposal is able to track the diffusion of continuous objects with high energy-efficiency.

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Acknowledgments

The study was supported by the Qing Lan Project, the National Natural Science Foundation of China (under Grant No. 61572172), the Fundamental Research Funds for the Central Universities (No. 2016B10714), Open fund of State Key Laboratory of Acoustics (no. SKLA201706), the Changzhou Sciences and Technology Program (No. CE20165023 and No. CE20160014), and the Six talent peaks project in Jiangsu Province (No. XYDXXJS-007).

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Authors and Affiliations

  1. College of Internet of Things Engineering, Hohai University, Changzhou, China

    Li Liu, Guangjie Han & Jiawei Shen

  2. State Key Laboratory of Acoustics, Institute of Acoustics Chinese Academy of Sciences, Beijing, China

    Guangjie Han

  3. School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China

    Wenbo Zhang

  4. Jiangsu Xin Zhongtian Plastic Industry Co., Ltd., Changzhou, China

    Yuxin Liu

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  1. Li Liu
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Correspondence to Guangjie Han.

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Liu, L., Han, G., Shen, J. et al. Diffusion Distance-Based Predictive Tracking for Continuous Objects in Industrial Wireless Sensor Networks. Mobile Netw Appl 24, 971–982 (2019). https://doi.org/10.1007/s11036-018-1029-8

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