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
Volume
21
Issue
3
Pages
849 - 861
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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