Resource Optimization And Allocation For Internet Of Thing Assisted By Unmanned Aerial Vehicle

The Internet of Things(Io T)is an extended network based on the Internet and communication networks,as well as an extension of Internet services.Its emergence enables information to be communicated and interact between different objects and reduce human intervention.With the increasing development of wireless networks,mobile communications and sensor devices,it provides strong support for the construction of the system architecture of the Io T in different fields.Although many achievements have been made in the application research of Io T technology,the limited resources of Io T devices,poor communication network quality and remote deployment have brought new challenges to data processing and communication security in the Io T.For example,the local processing of computing intensive tasks,and the unnecessary network overhead caused by excessive link load during computation offloading.Currently,UAV technology and mobile edge computing(MEC)can be combined to quickly respond to unexpected disruptions in Io T communication and expand the range of communications.At the same time,computing and storage resources are pushed to the edge of the network by MEC.The characteristics of high-efficiency computing and low-latency response of MEC greatly improve the quality of service.In addition,considering that UAV-assisted Io T is vulnerable to security attacks such as illegal eavesdropping and data tampering,the introduction of blockchain technology can ensure user privacy and data integrity.Based on the above analysis,starting from the construction of UAV-assisted Io T system model,resource optimization strategies of energy consumption model and latency model are studied.The specific research contents are as follows:(1)Optimal scheduling of resources for UAV-assisted Io T system based on MECFor the problem of the interruption of communication links in the Io T,and the limitation of computing resources and battery capacity of Io T devices,an UAV-assisted Io T system model based on MEC is constructed.When the transmission link of Io T devices is destroyed,the UAV as the relay node will calculate the collected tasks and then transmit the results to the wireless access point.On this basis,the total energy consumption of the system is minimized by jointly optimizing the time resource allocation of data offloading,data calculation and data transmission.Then,considering the complexity of the optimization problem,a distributed algorithm based on alternating direction multiplier method is used to obtain the optimal value.Simulation results show that reasonable time resource allocation can effectively reduce the time and energy consumption of data processing.In addition,compared with other schemes,the proposed scheme significantly improves the system performance.(2)Optimal scheduling of resources for UAV-assisted Io T system with MEC and blockchainIn order to solve the problems of privacy disclosure and data loss in the process of data transmission,an UAV-assisted Io T system model integrating MEC and blockchain is constructed.UAV is introduced into the Io T as a relay node to transfer complex computation tasks from the Io T devices to the base station.At the same time,the base station acting as the blockchain node is responsible for processing the computation offloading records from the MEC system.Then,based on the proposed system architecture,an optimization problem is formulated to achieve the optimal trade-off between system energy consumption and computation latency by jointly considering computation offloading decision,spectrum resource allocation and computing resource allocation.Through variable relaxation and substitution of product term,the original optimization problem of mixed integer is transformed into convex problem.Then the problem is decomposed and solved by a distributed algorithm based on alternating direction multiplier method.Simulation results show that the proposed scheme can converge quickly.In addition,compared with other schemes,the proposed scheme significantly reduces the system energy consumption and the computation latency of node consensus,while avoiding the unnecessary time overhead in the process of data computation and transmission.