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

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 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes (approximate to 9%).

Automated summary

This article proposes an algorithm for continuous object boundary tracking in industrial wireless sensor networks. The algorithm utilizes the collective intelligence and machine learning capability within the sensor nodes. By determining the upper bound of the event region covered by continuous objects and performing a binary tree-based partition, the algorithm achieves boundary tracking. Simulation results show that the algorithm reduces the number of nodes required for boundary tracking by at least 50%.