Boundary tracking of continuous objects based on binary tree structured SVM for industrial wireless sensor networks
journal contributionposted on 01.10.2020 by Li Liu, Guangjie Han, Zhengwei Xu, Jinfang Jiang, Lei Shu, Miguel Martinez-Garcia
Any type of content formally published in an academic journal, usually following a peer-review process.
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%).
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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
- Mechanical, Electrical and Manufacturing Engineering