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Boundary tracking of continuous objects based on binary tree structured SVM for industrial wireless sensor networks

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journal contribution
posted on 01.10.2020 by Li Liu, Guangjie Han, Zhengwei Xu, Jinfang Jiang, Lei Shu, Miguel Martinez-Garcia
Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50%. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes (≈ 9%).

Funding

National Key Research and Development Program, No.2017YFE0125300

Jiangsu Key Research and Development Program, No.BE2019648

Fundamental Research Funds for the Central Universities, B200201035

State Key Laboratory of Acoustics, Chinese Academy of Sciences, SKLA202004

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Mobile Computing

Pages

1 - 1

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-08-25

ISSN

1536-1233

eISSN

1558-0660

Language

en

Depositor

Dr Miguel Martinez Garcia . Deposit date: 30 September 2020

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