Research On Mobile Edge Computing Technology For Information Age Perception

In recent years,with the development of 5G technology,more and more real-time applications such as face recognition,internet of vehicles,automatic driving,telemedicine and so on come into people’s view.These applications usually have strict requirements on the freshness of information,different from the traditional communication delay,the freshness of information represents the timeliness of update in a network process from the perspective of destination.How to measure the freshness of information effectively has become an increasingly concerned topic for scholars.In this context,age of information(Ao I),as a novel metric to measure the information freshness,has attracted wide attention recently.It is defined as the time elapsed since the latest received data packet is generated at source.The Ao I metric jointly considers the time interval between two consecutive samples,transmission delay,and computation delay,which fully captures the temporal characteristics of the three steps for obtaining a status update,namely task generation,task offloading,and task execution,and this is different from the existing system design which aims to the energy consumption or transmission delay minimization.On the other hand,the applications mentioned above are often updated with computation-intensive and age-sensitive tasks but the limited computing resources of the local server may not be able to meet the low computing latency.Mobile edge computing(MEC)technology is often considered as the main technology to execute agesensitive and computation-intensive tasks due to the sufficient computing resources and closed to users.In this paper,we studied the impact of MEC network parameters on optimal offloading percentage and the relationship between devices offloading energy consumption and age of information.Firstly,we introduce the key concepts and technologies that support the research work in this paper,such as age of information and mobile edge computing technology.Secondly,this paper briefly introduces the research status of Ao I and age-aware mobile edge computing networks.Then,a single-source mobile edge computing network model is established to minimize the age of information by optimizing the offloading percentage.Based on the analysis results,we focus on the parameters of MEC network,such as packet size,the required number of CPU cycles,average data rate,the MEC server computing capacity and the local server computing capacity,etc.,we verified there is an optimal offloading percentage to minimize the average Ao I when the other parameters are fixed.At the same time,the influence of MEC network parameters on the age of information and the optimal offloading percentage are explored comprehensively,and obtained several interesting conclusions.This work plays a certain guiding role for the subsequent research work.Then,in the same MEC network model,the multi-objective optimization problem of device transmission energy consumption and age of information is established.The compromise relationship between the age of information single-source and equipment energy consumption was verified by the multi-objective optimization algorithm.The third work modeled the age-aware MEC network with two devices,which is a multi-slot system.We minimize the age of information by jointly optimizing energy consumption,computation offloading under the average energy constraint at each device.By using Lyapunov optimization technique,the long-term stochastic optimization problem is transformed into a single time slot deterministic optimization problem,and the closed-form solution of the optimal offloading energy consumption is obtained.The simulation results showed that when two users compete for age of information,the system will give priority to the age-sensitive users,and the energy-sensitive users can choose not to update to save energy.In addition,a compromise relationship between Ao I and device energy consumption is verified from the work.In conclusion,the research work about Ao I in this paper has certain reference value for exploring Ao I in mobile edge computing.The work can also be extended to multi-source scenarios,using partial offloading scheme to explore the influence of different offloading percentage of devices on system performance.In addition,it is also worth studying the influence of buffer-aided technology on Ao I in multi-source MEC network.