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Scientific congresses, symposiums and conference proceedings : Unpublished conference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 Wireless Communications and Networking Conference (WCNC)
26-03-2023 to 29-03-2023
Institute of Electrical and Electronics Engineers
Scotland, UK
[en] Device-free localization ; deep learning ; data dimensionality reduction ; convolutional deep belief network ; autoencoder ; wireless sensor networks
[en] Location-based services are witnessing a rise in popularity owing to their key features of delivering personalized digital experience. The recent developments in wireless sensing techniques make the realization of device-free localization (DFL) feasible within wireless sensor network (WSN) architectures. The DFL is an emerging technology that utilizes radio signal information for detecting and positioning a passive movable target without attached devices. However, determining the characteristics of the massive raw signals and extracting meaningful discriminative features relevant to the localization are highly intricate tasks due to the different patterns associated with different locations. To overcome these issues, deep learning (DL) techniques can be utilized here owing to their remarkable performance gains in similar practical problems. In this direction, we propose a DFL framework consists of multiple convolutional neural network (CNN) layers along with deep autoencoders based on the restricted Boltzmann machines (RBM) to construct a convolutional deep belief network (CDBN) for features recognition and extracting. Each CNN layer has stochastic pooling to sample down the feature map and reduced the dimensions of the required data without losing important information. This dimensionality reduction can alleviate the heavy computation while ensuring precise localization. The proposed framework is validated using real experimental dataset. The results show that the proposed model is able to achieve a high accuracy of 98% with reduced data dimensions and low signal-to-noise ratios (SNRs).
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