The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency‐flat/‐selective fading channels with/without Doppler shift are derived. Simulation results show that the proposed system can achieve reasonably low BERs in an additive white Gaussian noise channel as well as a Rayleigh fading channel. More importantly, the proposed system shows good performance in resisting eavesdropping, since the transmitted sequence appears to be a Gaussian noise which is almost always inevitable in the process of wireless communications.

In this study, the parameter of a symmetric α ‐stable (S α S) noise sequence is modulated by the binary message sequence to achieve a secure communication system. The characteristic exponent ‘ α ’ of an S α S noise sequence carries the binary information. In order to recover the binary message sequence at the receiver, the authors propose a logarithmic moments estimator to estimate the characteristic exponent ‘ α ’ of the transmitted noise sequence. The optimal decision threshold and the minimum theoretical bit error ratio are derived. It is shown that the simulation results of the presented logarithmic moments estimator are consistent with the analytical results. Moreover, this estimator shows better performance and lower computational complexity than the conventional SINC estimator based on the fractional low‐order moment method.

With the rapid development of Internet of Things (IOT) applications, the performance of existing wireless localization methods under non-line-of-sight (NLOS) transmission environments is seriously challenged. Therefore, this paper proposes a new definition of residual, i.e., the angle residual. The key target of angle residual is to detect line-of-sight (LOS) links from measured and calculated angle parameters. The target position is then estimated by two-step weighted least squares (TS-WLS) algorithm, where only positioning parameters of LOS link are included. Simulation shows that the proposed algorithm performs better than traditional algorithms, especially when the mobile node (MN) position and the NLOS link distribution are randomly generated. Therefore, the proposed method effectively improves the positioning performance and enhances the localization stability in real NLOS transmission environments.

Information security is a key issue involved in the utilization of different technological innovations, such as the Internet of Things (IoT). This study proposes a new covert communication scheme in which the transmitted waveform modulated by the covert bit is statistically similar to the background noise to achieve information security. The impulsive noise in many communication fields can be well modeled with a symmetrical $\alpha $ -stable distribution. Thus, the proposed scheme exploits skewed $\alpha $ -stable distributions to conduct the covert bit. The polarity transition of the skewness parameter within the first and second halves of a bit period represents a covert bit. The resultant signal exhibits a near-symmetrical behavior similar to ambient interference to achieve concealment. The sample skewness estimator based on the signed fractional lower order moment is used to determine the binary message at the receiver. Then, the expression of the skewness estimator and its probability density function are derived. With the aid of the hard decision, the theoretical expression of the bit error rate of the proposed communication system is obtained. The proposed scheme works at the physical layer with the advantages of simple structure, easy implementation, and extreme suitability with low-cost, resource-limited IoT devices exhibiting low-rate transmission.

Vibration signal analysis is the most common technique used for mechanical vibration monitoring. By using vibration sensors, the fault prognosis of rotating machinery provides a way to detect possible machine damage at an early stage and prevent property losses by taking appropriate measures. We first propose a digital integrator in frequency domain by combining fast Fourier transform with digital filtering. The velocity and displacement signals are, respectively, obtained from an acceleration signal by means of two digital integrators. We then propose a fast method for the calculation of the envelope spectra and instantaneous frequency by using the spectral properties of the signals. Cepstrum is also introduced in order to detect the unidentifiable periodic signal in the power spectrum. Further, a fault prognosis algorithm is presented by exploiting these spectral analyses. Finally, we design and implement a visualized real-time vibration analyzer on a Raspberry Pi embedded system, where our fault prognosis algorithm is the core algorithm. The real-time signals of acceleration, velocity, displacement of vibration, as well as their corresponding spectra and statistics, are visualized. The developed fault prognosis system has been successfully deployed in a water company.