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Learning Wireless Sensor Networks for Source Localization
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DOI：10.3390/s19030635.png)
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Abstract
En 中文
Source localization and target tracking are among the most challenging problems in wireless sensor networks (WSN). Most of the state-of-the-art solutions are complicated and do not meet the processing and memory limitations of the existing low-cost sensor nodes. In this paper, we propose computationally-cheap solutions based on the support vector machine (SVM) and twin SVM (TWSVM) learning algorithms in which network nodes firstly detect the desired signal. Then, the network is trained to specify the nodes in the vicinity of the source (or target); hence, the region of event is detected. Finally, the centroid of the event region is considered as an estimation of the source location. The efficiency of the proposed methods is shown by simulations.
Keywords:
Internet of Things
quadratic programming
region of event detection
source localization
support vector machine (SVM)
target tracking
twin support vector machine (TWSVM)
wireless sensor network (WSN)
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