No abstract

The Internet of Things (IoT) will feature pervasive sensing and control capabilities via the massive deployment of machine-type communication devices in order to greatly improve daily life. However, machine-type communications can be illegally used (e.g., by criminals or terrorists) which is difficult to monitor, and thus presents new security challenges. The information exchanged in machine-type communications is usually transmitted in short packets. Thus, this paper investigates a legitimate surveillance system via proactive eavesdropping at finite blocklength regime. Under the finite blocklength regime, we analyze the channel coding rate of the eavesdropping link and the suspicious link. We find that the legitimate monitor can still eavesdrop the information sent by the suspicious transmitter as the blocklength decreases, even when the eavesdropping is failed under the Shannon capacity regime. Moreover, we define a metric called the effective eavesdropping rate and study the monotonicity. From the analysis of monotonicity, the existence of a maximum effective eavesdropping rate for a moderate or even high signal-to-noise (SNR) is verified. Finally, numerical results are provided and discussed. In the simulation, we also find that the maximum effective eavesdropping rate slowly increases with the blocklength.

Distributed optical fiber acoustic sensing (DAS) can serve as an excellent tool for real-time condition monitoring of a variety of industrial and civil infrastructures. This paper presents a belt conveyor roller fault abnormal monitoring method based on DAS, for the low accuracy and efficiency of the existing belt conveyor rollers fault detection. This method uses the Rayleigh Backscatter of coherent pulsed light to detect and reconstruct the fault signal, and proposes a method based on the combination of power spectrum features and peak detection to recognize and locate abnormal signals under intense background noise. The field test verifies the effectiveness of the real-time monitoring scheme of the industrial conveyor belt system, with a detection accuracy rate of over 87% for simulated fault signals, and a location accuracy of ±2.5 m. It provides a new passive distributed monitoring method for the all-weather structural health monitoring of the rollers in the industrial belt conveyor systems.