Secured data transmissions in corporeal unmanned device to device using machine learning algorithm

【Author】 Shitharth, S.; Yonbawi, Saud; Manoharan, Hariprasath; Shankar, Achyut; Maple, Carsten; Alahmari, Sultan

【Source】PHYSICAL COMMUNICATION

【影响因子】2.379

【Abstract】Cyber-physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today's sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules. & COPY; 2023 Published by Elsevier B.V.

【Keywords】Machine Learning (ML); Blockchain technology; Device-to-Device (D2D); Unmanned Aerial Vehicles (UAV)

【发表时间】2023 AUG

【收录时间】2023-08-19

【文献类型】

【主题类别】

--