Secure transmission of wireless energy-carrying communication systems for the Internet of Things

Abstract

The Internet of Things, as an important part of important data aggregation, forwarding and control, is often subject to risks such as eavesdropping or data loss due to the huge amount of received data. Based on this, this paper introduces the GA-LM-BP algorithm, BP network, and LM-BP algorithm deep learning to optimize the data received by the Internet of Things, and selects the most suitable communication mode optimization algorithm. The experimental results show that the accuracy error of GA-LM-BP, BP and LM-BP algorithms shows a downward trend, from 0.029 to 0.011; the training time is reduced by 208 mins, and the training speed is increased to 74%, indicating that GA-LM-BP deep learning Excellent performance in the security and confidentiality of data transmission in the Internet of Things. In addition, we further analyzed GA-LM-BP from COP, SOP and STP to verify its reliability and safety.

Secure transmission for IoT wireless energy-carrying communication systems

“…In Fig 8 , the efficiency of the proposed method has been evaluated in terms of the mentioned criteria and the results have been compared with GA-LM-BP, LM-BP and BP methods in [ 32 ].…”

“…Finally, the RE criterion indicates the rate of difference between the observed and the actual values, which is calculated by dividing the difference between the observed values and the In Fig 8, the efficiency of the proposed method has been evaluated in terms of the mentioned criteria and the results have been compared with GA-LM-BP, LM-BP and BP methods in [32].…”