Research On Radio Resource Management Based On Mobile Edge Computing In Massive Connected IoT Scenario

With the development of the massive Internet of Things,research on radio resource management based on Mobile Edge Computing in the IoT scenario has attracted widespread attention,which is one of the main directions for the development of radio communication in the future.In the era of massive connectivity of the Internet of Things,massive amounts of data will inevitably occur.If these massive amounts of data are all offloaded to the cloud core network,the cloud computing network transmission load will increase dramatically,resulting in longer network delays and greatly reducing the user experience.Mobile Edge Computing(MEC),as a key technology to improve user experience in future 5G networks,effectively reduces network transmission delay and energy consumption by sinking computing power to mobile edge nodes.However,the mobile edge server has limited computing resources and cannot offload huge amounts of data to the mobile edge server.If there are idle and computationally rich terminal devices around,the device-to-device(D2D)communication mode and the MEC server can be jointly unloaded and cached to further improve the computing power of the cellular network.Therefore,this thesis studies how to effectively improve the radio resource management of mobile edge computing under the massive connected IoT scenario.The main innovations are as follows:1)A task offloading strategy is proposed for a single-MEC-server and multi-user scenario.For computationally intensive or delay-sensitive applications,it is proposed that this kind of tasks can be jointly offloaded to the idle terminal with abundant computing resources or the MEC server.This problem is formulated as a potential game,and the potential function is furtherly obtained through the cooperation-puppet model,,and then the best response is utilized to achieve the optimal solution.The algorithm yields an optimal offloading strategy.The simulation analysis shows that the proposed best response-based MEC-D2D joint unloading algorithm has good convergence,which significantly improves the amount of data transmitted and reduces the energy consumption of the task.2)A task caching strategy is proposed for a single MEC server multi-user scenario.For the current popular video stream files,it is proposed that the idle and computing resources terminal help the MEC server to co-cache through D2D communication.The MEC server uses a different popularity video file price and the set total reward to propose a Stackelberg Game.(Stackelberg Game,SG)model,establishing a two-layer game model of leaders and followers.3)At the same time,set up a certain incentive mechanism to encourage D2D devices to help MEC servers to collaborate and cache.Based on the SG,the inverse SG model is used to analyze the SG model,and the cost function of the leader MEC server and the cost function of the follower terminal are solved respectively.Finally,the scheme proposed in this paper is verified by simulation.It greatly stimulated the enthusiasm of D2D devices to participate in collaborative caching.