Research On Task Offloading Method With Minimized Energy Consumption In Mobile Edge Computing Network

With the development of the Internet of Things(Io T)and 5G technology,an increasing number of smart devices and sensors are connected to the network,generating a vast amount of data.This data needs to be processed and analyzed at the edge to achieve fast response and real-time decision-making.Mobile edge computing sinks the computing,storage,and network resources of cloud computing and networks to the edge of the mobile network,providing users with the communication,storage,and computing resources needed to process data and services at the edge.In mobile edge computing,a major challenge is how to migrate workflow tasks to mobile edge computing nodes for execution.Therefore,this paper focuses on the scheduling problem of workflows,considering factors such as task priority,available resources,and offloading nodes to achieve reasonable task allocation and optimization.Additionally,during the task offloading process,there may be malicious eavesdroppers who can use network interception techniques to eavesdrop on communication between mobile devices and edge nodes,thereby obtaining sensitive user information.Physical layer security techniques can utilize the transmission characteristics of wireless channels to ensure data security.Therefore,minimizing system energy consumption under the constraints of computational latency and secure physical layer transmission becomes a worthwhile topic to explore.This paper delves into issues such as workflow tasks,offloading strategies,energy optimization,channel models,and physical layer security.The main research of this paper is as follows:(1)A multi-user workflow task offloading decision and scheduling scheme is proposed for the multi-user workflow scheduling problem in mobile edge computing environments.Firstly,the offloading and scheduling of workflow tasks are modeled,based on which the calculation expressions for the total system delay and total energy consumption are obtained.Then,an adaptive genetic algorithm is employed,and improvements are made in the encoding,crossover,mutation and other operations to solve the problem of minimizing the total system energy consumption under the time delay constraint,and to determine the optimal offloading strategy and scheduling scheme.Finally,simulation experiments show that the proposed algorithm can effectively reduce the total system energy consumption compared to other offloading algorithms under conditions such as the number of users,the number of workflow tasks,and task workload.(2)A joint task offloading and resource allocation mechanism is proposed to minimize the system energy consumption in the partial offloading mode of a multi-user NOMA-MEC system.The aim is to minimize the total system energy consumption while considering constraints such as delay and the probability of confidentiality interruption.As the problem is non-convex,an iterative optimization algorithm is proposed based on the alternating optimization algorithm to solve it.Simulation results show that this scheme can effectively reduce system energy consumption.