Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

The long-range wide area network (LoRaWAN) has recently emerged as one of the most potential technologies for the mobile Internet of Things (IoT) applications. In a LoRaWAN, the networkmanaged adaptive data rate (ADR) method is responsible for managing resource allocation (i.e., spreading factor (SF) and transmit power (TP)) to end-devices (EDs) through ADR commands. When an ED is mobile and receives a new configuration (i.e., SF and TP) from the network server, the propagation scenario could have been dramatically changed. Hence, the SF and the link budget will no longer be valid, which results in packet loss and massive retransmissions. Therefore, we propose a mobility-aware resource (SF) assignment scheme (M-ASFA), which aims to allocate the best SF to IoT-enabled mobile EDs at each uplink transmission by considering the strength of the signals that a gateway receives from the EDs. The simulation results demonstrate that, in our proposed M-ASFA solution, the SF is assigned to mobile EDs by proactively responding to the mobility of the EDs. This proactive behavior of the proposed scheme enhances the packet success ratio by significantly reducing the impact of interference, retransmissions, and packet loss when compared with the LoRaWAN-based ADR.

Indoor positioning systems (IPSs) have been discussed for use in entertainment, home automation, rescue, surveillance, and healthcare applications. In this paper, we present an IPS that uses an impulse radio-ultra-wideband (IR-UWB) radar network. This radar network system requires at least two radar devices to determine the current coordinates of a moving person. However, one can enlarge the monitoring area by adding more radar sensors. To track moving targets in indoor environments, for example, patients in hospitals or intruders in a home, signal processing procedures for tracking should be applied to the raw data measured using IR-UWB radars. This paper presents the signal processing method required for robust target tracking in a radar network, that is, an iterative extended Kalman filter- (IEKF-) based object tracking method, which uses two IR-UWB radars to measure the coordinates of the targets. The proposed IEKF tracking method is compared to the conventional extended Kalman filter (EKF) method. The results verify that the IEKF method improves the performance of 2D target tracking in a real-time system.

The existing radio frequency-based positioning approaches widely used for indoor localization-based service (LBS) are fingerprinting and trilateration and their integration with the inertial sensors-based dead reckoning system. However, these localization methods have practical limits and challenges due to unstable signal strength, the cost of offline workload, computational complexity, terminal device heterogeneity, and accumulated sensor error. We propose a smartphone-based indoor localization system using weighted Spearman's foot-rule (WSF)-based probabilistic fingerprinting for reliable smartphone localization service. This localization system adopts a real-time fingerprinting position error estimation approach realizing an adaptive extended Kalman filter (AEKF) to integrate the proposed fingerprinting localization with inertial measurement unit (IMU)-based localization. This proposed WSFbased smartphone localization uses a Gaussian process regression (GPR)-based signal prediction module to deal with fingerprinting localization's offline workload. Furthermore, the smartphone localization system's expected high computational complexity is controlled by employing a data-clustering module. The proposed WSF also employs a rank vector that helps mitigate the effect of terminal device heterogeneity. The proposed localization system is experimentally evaluated at two different representative indoor environments. Experimental results obtained by real field deployment show that the mean error is 2.06 m in an elongated hallway corridor and 3.47 m in the crowded and well-furnished wide area.