Abstract
In this study, we consider a security efficiency maximization problem in a multiple unmanned aerial vehicle (UAV)-aided system with mobile edge computing (MEC). Two kinds of UAVs, including multiple computing UAVs (CUAVs) and multiple jamming UAVs (JUAVs), are considered in this system. CUAVs would receive partial computation bits and send the computation results to ground users. JUAVs do not undertake computing tasks and only send interference signals to counter potential ground eavesdroppers. We jointly optimize the ground user scheduling, UAV power, and UAV trajectory to maximize the security efficiency. The original problem is non-convex and difficult to solve. We first use the Dinkelbach method combined with continuous convex approximation technology, and then propose three corresponding subproblems, including user scheduling subproblem, UAV power subproblem, and UAV trajectory problem. Further, we apply the branch and bound method to solve the user scheduling subproblem, and optimize the two remaining subproblems by introducing auxiliary variables and Taylor expansion. The simulation results show that the proposed scheme can obtain better secure off-loading efficiency with respect to the existing schemes.
Highlights
The application of 5G mobile communication technology has brought many conveniences to people’s life and production activities
unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems need to meet the requirements of low energy consumption and high computing bits (Loke, 2015; Zhou Z. et al, 2018)
In the study by Zhou F. et al (2018), the problem of maximizing computational efficiency is studied by jointly optimizing the computational shunt and the UAV trajectory design in the UAV-assisted MEC network
Summary
The application of 5G mobile communication technology has brought many conveniences to people’s life and production activities. Data cleaning based on mobile edge nodes can maintain the reliability and integrity of data while improving the efficiency of data cleaning and reducing the energy consumption of industrial sensor cloud systems (Wang et al, 2020). UAV-assisted MEC expands the coverage area of edge computing and improves computing capabilities. In order to improve traffic efficiency, the UAV-assisted MEC system with three-layer integrated architecture is used to solve the dynamic optimization problem of energy perception (Zhang et al, 2018). UAV-assisted MEC systems need to meet the requirements of low energy consumption and high computing bits (Loke, 2015; Zhou Z. et al, 2018). In the study by Zhou F. et al (2018), the problem of maximizing computational efficiency is studied by jointly optimizing the computational shunt and the UAV trajectory design in the UAV-assisted MEC network.
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