Conceitos Básicos
This work presents a multi-objective optimization approach to jointly optimize task offloading, computation resource allocation, and UAV trajectory control in a multi-UAV-assisted mobile edge computing system, aiming to minimize the total task completion delay, reduce the total UAV energy consumption, and maximize the total amount of offloaded tasks.
Resumo
The paper proposes a multi-UAV-assisted mobile edge computing (MEC) system, where multiple UAVs equipped with MEC servers provide computing services to mobile users. The authors formulate a multi-objective optimization problem to jointly optimize task offloading, computation resource allocation, and UAV trajectory control, with the goals of minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks.
The optimization problem is proven to be a non-convex and mixed-integer non-linear programming (MINLP) problem, which is challenging to solve. To address this, the authors propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach. The original problem is split into three sub-problems: task offloading, computation resource allocation, and UAV trajectory control, which are solved individually using different methods.
Specifically, for the task offloading sub-problem, the authors employ distributed splitting and threshold rounding methods to solve the binary offloading decision variables. For the computation resource allocation sub-problem, the Karush-Kuhn-Tucker (KKT) method is adopted. For the UAV trajectory control sub-problem, the successive convex approximation (SCA) method is used.
The simulation results demonstrate that the proposed JTORATC approach outperforms several benchmark schemes in terms of objective function value, total task completion delay, and total UAV energy consumption. The authors also find that the proposed algorithm has better scalability in the considered scenarios.
Estatísticas
The total task completion delay can be calculated as the sum of the local processing delay and the offloading delay to UAVs.
The total UAV energy consumption includes the energy consumed for task execution and UAV flight.
The total amount of offloaded tasks is the sum of the task sizes offloaded to UAVs.
Citações
"To address the abovementioned challenges, this work proposes a multi-objective optimization problem of task offloading, computation resource allocation, and UAV trajectory control for multi-UAV assisted MEC systems."
"We propose a joint task offloading, computation resource allocation and UAV trajectory control (JTORATC) approach to solve the problem."
"Simulation results show that the proposed JTORATC achieves superior performance in terms of objective function value, total task completion delay, and total UAV energy consumption compared to several benchmark schemes."