Research On Access Network And Localization Of Fog Computing

Internet of Things(IoT)is developing rapidly.It is the future direction of network development.In the future communication scenarios,through the perception of the physical world and seamlessly connected heterogeneous intelligent device network,IoT can establish information exchange and intelligent control between people and things,things and things.However,the traditional cloud computing architecture has been unable to meet the various needs of IoT applications for data storage,data calculation and network control.As a new computing paradigm,fog computing has made up for the gap of cloud computing in the context of IoT.Based on traditional cloud architecture and terminal equipment capabilities,it extends all kinds of cloud computing services to the edge of network in order to meet location-related,large-scale distribution and timedelay sensitive application requirements.Key factors such as time delay,power consumption and location directly affect the development of IoT applications.However,massive terminal equipment access and frequent computing requests will also bring many challenges to the fog computing access process and calculation offload.At the same time,acquiring object location information is also a necessary condition for IoT technologies such as vehicle networking,disaster monitoring and intelligent wearable devices.Therefore,how to solve the location problem in fog computing system and improve the fog radio access networks(F-RANs)performance have become a hot research topic.This paper is based on access selection,computation offload and localization in fog computing system.Based on F-RANs,an access optimization method based on joint delay and fog-cloud computing offloading method based on terminal power consumption are proposed.Combined with the technology of positioning for wireless sensor networks(WSN),a weighted factors localization algorithm is proposed.The main research contents are as follows:Optimization method of F-RANs.Aiming at the issue of access point selection in fog computing,a network architecture and communication model are established respectively.The whole system is composed of wireless transmission process,fog calculation process,fog-to-cloud transmission process and cloud computing process.Joint access delay consists of transmission delay and computing delay.The proposed algorithm aims to minimize the joint delay of F-RANs.The simulation results show that with the increase of the number of user terminals,the optimization method based on joint delay is better than the optimization method based on single optimization of transmission delay,and the access delay of user terminals is significantly reduced.Computation offloading method for hybrid cloud-fog computing.The problem of minimizing power consumption of user terminals during fog-cloud computing offloading is studied.Based on the terminal transmission power and the maximum tolerance delay constraint,the energy consumption of user terminals is minimized where the transmission power allocation and task allocation factors of each terminal are obtained.The optimal target is to minimize the sum of the user terminal's energy consumption in each phase under the condition of satisfying the maximum tolerance delay and the maximum transmission power.Alternating convex optimization method is used to solve the problem.The simulation results show that the hybrid fog-cloud computing unloading method proposed can minimize the terminal energy consumption.Localization of fog-supported WSN.Firstly,a three-layer network model is established for fog-supported WSN.Sensor nodes include fog sensor nodes,anchor nodes and common nodes.According to the range-based localization technologies and three different types of sensor nodes,this paper proposes a weight factors localization algorithm.After the distance between nodes is obtained by using the improved RSSI distance estimated model,the coordinates of common nodes are solved by mathematical methods.In addition,this paper also proposes a special nodes localization scheme.According to a lot of simulated results,the performance of the algorithm is evaluated.The results show that the proposed algorithm is superior to the other two existing algorithms.