Deep Learning-Based Device-Free Localization in Wireless Sensor Networks

Scientific Conference (2023)

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 ... [more ▼]

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). [less ▲]

Energy Harvesting from Jamming Attacks in Multi-User Massive MIMO Networks

in IEEE Transactions on Green Communications and Networking (2022)

5G communication systems enable new functions and major performance improvements but at the cost of tougher energy requirements on mobile devices. One of the effective ways to address this issue along ... [more ▼]

5G communication systems enable new functions and major performance improvements but at the cost of tougher energy requirements on mobile devices. One of the effective ways to address this issue along with alleviating the environmental effects associated with the inevitable large increase in energy usage is the energy-neutral systems, which operate with the energy harvested from radio-frequency (RF) transmissions. In this direction, this paper investigates the notion of harvesting the ambient RF signals from an unusual source. Specifically, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. Two optimal time-switching schemes are also proposed based on maximum sum rate and user-fairness criteria. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can exploit the surrounding RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Finally, numerical results illustrate the effectiveness of the derived closed-form expressions through simulations. [less ▲]