Secure transmission for IoT wireless energy-carrying communication systems.

The wireless energy-carrying communication method for the Internet of Things (IoT) presents several difficulties for information security such as eavesdropping or data loss. To solve these issues, this paper presents a new secure transmission method for IoT wireless energy-carrying communication systems. In this method, first the secret message is turned into a word, delivered to the intended recipient and unlawful listener, respectively, and the received message is characterized as an entropy function. The message is iteratively solved using the block coordinate descent technique, and for each iteration, a digital baseband signal containing the receiver's secret message symbol and the matching beamforming vector is delivered. By concurrently optimizing the transmit beamforming vector, the noise covariance matrix, and the receiver power allocation factor based on a design that complies with the security rate and energy acquisition limitations for each receiver, the overall system transmit power is reduced. The Lagrangian method is used to solve the secure transmission problem of the communication system based on an iterative block coordinate descent algorithm, as well as to change the nonconvex problem into a convex problem and precisely derive the upper and lower bounds of the original transmission problem. In comparison to the conventional policy transmission scheme, the experimental results demonstrate that the DIPS (Digital Image Processing System) scheme can increase the STP (Signaling Transfer Point) by approximately 34.16 percent in the eavesdropper independent eavesdropping and joint eavesdropping scenarios. The usefulness of the secure transmission strategy for wireless energy-carrying communication systems is confirmed by this investigation.

Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation.

It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters.