Abstract
As fixed edge computing systems can hardly meet the demand of mobile users for massive data processing, a computational resource allocation strategy using the genetic algorithm in UAV-assisted edge computing environment is proposed. First, a UAV-assisted mobile edge computing (MEC) system is designed to help users execute computation tasks through the UAV or relaying to the ground base station. Then, a communication model and a computation model are constructed to minimize the total system energy consumption by jointly optimizing the UAV offloading ratio, user scheduling variables, and UAV trajectory. Finally, the minimization of total system energy consumption is modeled as a nonconvex optimization problem and solved by introducing an improved genetic algorithm, so as to achieve a rational allocation of computational resources. Based on the experimental platform, the simulation of the proposed method is carried out. The results show that the total energy consumption is 650 J when the execution time is 110 s and the execution time is 17.5 s when the number of users is 50, which are both better than other comparison methods.
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