Research On Data Offloading For Mobile Crowdsensing Based On IoV

The development of sensors,communication technology and consumer electronics has given birth to Mobile Crowdsensing(MCS).By assigning sensing tasks to participants and collecting sensing data using their mobile devices,MCS can effectively reduce sensing cost and latency compared to traditional sensing schemes.However,large-scale MCS tasks have constantly increasing requirements for sensing quality,which are reflected in the improvement of Spatio-Temporal Coverage(STC),data quality,and real-time performance,which pose challenges to the communication and storage capabilities of MCS system.The increasing number of nodes participating in MCS and the ever-improving perception quality require MCS platforms to select appropriate participants within budget constraints to ensure sensing quality.At the same time,the sensing nodes generate huge sensing data traffic,frequent communication through the cellular network will increase the workload,and the licensed spectrum resources will be insufficient.This paper studies the participant selection problem and data offloading problem based on the Internet of Vehicles(IoV)for MCS.Firstly,the MCS technology based on the IoV and the data offloading technology in the IoV are introduced.Then,aiming at the optimization of the coverage quality of MCS,it is proposed to add static sensing nodes(Sensor Node,SN)on the basis of vehicles as sensing nodes,and use a combination of hybrid participants to cover the sensing area opportunistically,maximizing the STC.The process of selecting participants by the MCS platform is modeled as a Markov decision process,and a participant selection method based on Deep Reinforcement Learning(DRL)is proposed,which can further reduce the task cost while ensuring the quality of coverage.On the other hand,for the problem of sending data traffic and the delay tolerance time of different sensing tasks,an opportunistic vehicle networking data offloading scheme based on Roadside Unit(RSU)is proposed.An improved greedy algorithm is proposed for deploying RSU,and the effectiveness of the above algorithm in MCS coverage and data offloading is verified by vehicle trajectory set simulation.