Research On UAV-assisted Task Offloading And Resource Allocation In Maritime Internet Of Things

With the implementation of maritime Internet of Things projects such as "smart ocean" and "transparent ocean",China’s exploration of the Marine field has gradually changed from discrete to holistic.At present,the composition of maritime communication network has certain limitations,and it is difficult and expensive to deploy communication infrastructure in the ocean scene.Moreover,it is not suitable for the rapidly developing Marine Internet of Things system services.Therefore,a maritime communication solution that can provide big data,high speed and low cost is urgently needed.The characteristics of UAV assisted edge computing,such as low cost,strong mobility and fast response speed,make it more suitable for Marine environment.It is a feasible means to improve the performance of Marine communication network by deploying edge server in UAV.However,there are limitations of resources and energy consumption in applying UAV to Marine network.Therefore,this paper studies the application of multi-UAV-assisted edge computing technology to task offloading and resource allocation in the maritime Internet of Things.The specific work is as follows:In order to minimize the energy consumption of UAV in the scenario of lightweight computing data of small maritime Internet of Things,the utility function of minimizing the energy consumption of UAV side was designed based on the UAV-assisted edge computing architecture based on orthogonal frequency division multiple access,combined with the equipment time delay and energy consumption requirements of maritime users.On this basis,the binary decision variables of maritime user task offloading were established,and the problem of maritime user equipment task offloading decision and UAV computing resource allocation was optimized based on mixed integer nonlinear programming.An iterative bilateral matching game offloading decision method based on potential utility preference was proposed,and the method constantly searched for better potential matching relationship in the iterative process.Based on Lagrange multiplier method,the optimal solution of the computational resource allocation subproblem is derived.Simulation results show that compared with the UAV-oriented matching algorithm,the proposed algorithm can reduce the energy consumption of UAVoriented nodes under the premise of providing offloading service for maritime users’ equipment.Aiming at the problem of minimizing system energy consumption under the scenario of large amount of computing data in large-scale maritime Internet of Things,based on the UAVassisted edge computing architecture under non-orthogonal multiple access,the coupled constraints of task completion time limit,task offloading decision,power,computing resources and multi-dimensional variables of UAV trajectory were established,and the system energy consumption weighting minimization model was designed.A vertical hierarchical alternating iterative optimization scheme is proposed.A simulated annealing algorithm based on chaotic mapping is proposed for the outer integer programming problem.Combining with the ergodicity of Logistic mapping,the local optimal solution can be effectively removed.Further,the inner problem is decomposed into the user equipment transmitting power,computing resource allocation and UAV trajectory planning subproblems under a given task offloading decision scheme.However,the decomposed subproblems are still non-convex.Therefore,the constraint structure is transformed and the continuous convex approximation is used to approximate the problem as a convex optimization problem.The utility optimization of the system is approximated by two-layer algorithm and iterative optimization of subproblems.The simulation results show that compared with the scheme under orthogonal multiple access,the proposed joint optimization scheme not only reduces the total energy consumption of the system,but also has advantages in the optimization of task offloading cost.