Research On Marine Network Computing Offloading And Energy Collection Strategy Based On Edge Computing

With the advancement of China’s ‘smart ocean’ project,the marine industry has been developing continuously.Marine businesses such as marine transportation,marine environmental monitoring and resource exploration are diversified,resulting in more and more time-delay sensitive and computing intensive tasks.The demand for high reliability and low time-delay marine communication network is becoming increasingly obvious.However,traditional marine communication network has problems such as uneven coverage and inflexible configuration.It is increasingly difficult to meet the increasing maritime activity.Therefore,aiming at the problem of uneven density of marine nodes,this paper establishes the task offloading model based on mobile edge computing in offshore and pelagic scenes;In view of the difficulty of ocean energy transmission,the energy collection technology is introduced,and the hybrid energy supply strategy is proposed to solve the instability caused by the energy collection technology.In the solution method,the objective function is established around the two indexes of time delay and energy consumption,and the traditional bionic algorithm is improved to optimize the model solution.The main research work of this paper is as follows:(1)In this paper,a mobile edge computing offloading model with mixed energy supply is proposed for the problems of dense computing nodes and large energy cost in offshore areas.For the energy supply problem,the mobile edge server can supply energy by combining renewable energy with power grid to ensure the continuity of its service.Ship users use the energy collected by solar energy to supply energy,and use the ship shore power system to supplement energy.For the task offloading optimization problem,the model takes the minimization of time delay and energy consumption as the objective,establishes the objective function,proposing the dimensionality reduction optimization algorithm to optimize the local computing power and transmission power in turn,and simplifies the objective function to a one-dimensional multi constraint problem.The optimal data offloading ratio is obtained by improving the whale optimization algorithm to further obtain the optimal total system execution cost.The simulation based on Edge Cloud Sim experimental platform verifies the necessity of each part of the strategy proposed in this paper to improve the performance of the model,and the method proposed in this paper has the lowest total execution cost compared with other methods.(2)For the problems of complex ocean scene environment and lack of computing resources,a task offloading model between ship users is established.Aiming at the task offloading optimization problem,this paper selects the task offloading node based on the connectivity probability between the ship users to be offloaded and the idle ship users.In order to alleviate the data congestion problem,the orthogonal frequency division multiple access technology is used to divide the transmission channel,and the algorithm of matching the subtask with the subchannel is proposed.The ship user uses solar energy to supply energy,and proposes a penalty strategy for insufficient energy collection and a penalty strategy for task delay,so that the system can minimize the actual energy consumption and establish an objective function under the condition of satisfying the delay constraint.The local transmit power is optimized,and the gray wolf optimization algorithm is improved to obtain a better data offloading ratio.The simulation results of Edge Cloud Sim show that,compared with other methods,the proposed method can achieve the best performance while guaranteeing the delay.