Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Li Liu,Jinfang Jiang,Miguel Martinez-Garcia,Zhengwei Xu,Lei Shu,Guangjie Han

doi:10.1109/tmc.2020.3019393

Abstract

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 <i>hierarchical soft margin support vector machine</i> 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 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ( <inline-formula><tex-math notation="LaTeX">$\approx 9\%$</tex-math></inline-formula> ).

Highlights

P RODUCTION activities in petrochemical, plastics, and energy industries are always accompanied with the risk of normal accidents [1] which may result in leakage of chemical gases, radioactive contamination, oil spill and other continuous objects [2]
We propose a full binary tree structured network partition mechanism to achieve boundary area mapping, reducing the searching space of boundary nodes
Classification algorithms in [29]-[30], we provide the following remarks to explain why we select original softmargin support vector machine (SVM) as a prototype to be reformed for boundary tracking in Industrial Wireless Sensor Networks (IWSNs)

Summary

Introduction

P RODUCTION activities in petrochemical, plastics, and energy industries are always accompanied with the risk of normal accidents [1] which may result in leakage of chemical gases, radioactive contamination, oil spill and other continuous objects [2]. These continuous objects tend to spread over a wide region, which may cause poisoning, dangerous fire, or an explosion. By offering sensing services and ubiquitous networking to industrial systems, IWSNs show great potential in serving industrial safety before and after the occurrence of an accident. After the leakage of a toxic gas, using pervasive sensor nodes in IWSNs to acquire the concentration of gas in different positions, the distribution of leak gas can be visualized and emergency relief operation can be carried out orderly [7]

Results

Discussion

Conclusion