Paper Info
Title
Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks
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> ).
Year
DOI
Venue
2022
10.1109/TMC.2020.3019393
IEEE Transactions on Mobile Computing
Keywords
DocType
Volume
Industrial wireless sensor networks,continuous objects,boundary tracking,binary tree,support vector machines
Journal
21
Issue
ISSN
Citations 
3
1536-1233
1
PageRank 
References 
Authors
0.35
29
6
Name
Order
Citations
PageRank
Li Liu118112.42
Guangjie Han21890172.76
Zhengwei Xu351.76
Jinfang Jiang481042.80
Shu Lei52927216.78
Miguel Martinez-Garcia64412.85