Reference : Deep Learning-Based Device-Free Localization in Wireless Sensor Networks
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
Deep Learning-Based Device-Free Localization in Wireless Sensor Networks
Abdullah, Osamah mailto [Alma'moon University College, Baghdad, Iraq > Department of Electrical Engineering]
Al-Hraishawi, Hayder mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
IEEE Global Communications Conference (Globecom)
Institute of Electrical and Electronics Engineers
Rio de Janeiro
[en] Device-free localization ; deep learning ; data dimensionality reduction ; convolutional deep belief network ; autoencoder ; wireless sensor networks
[en] Location-based services (LBS) are witnessing a rise in popularity owing to their key features of delivering powerful and personalized digital experiences. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible in wireless sensor networks. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive target while the target is not equipped with a wireless device. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks. Thus, deep learning (DL) techniques can be utilized to address the DFL problem due to their unprecedented performance gains in many practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data for precise localization. The proposed framework is validated using real experimental dataset. The results show that our algorithm can achieve a high accuracy of 98\% with reduced data dimensions and low signal-to-noise ratios (SNRs).
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