A blockchain-based framework for data quality in edge-computing-enabled crowdsensing  

作　　者：Jian AN Siyuan WU Xiaolin GUI Xin HE Xuejun ZHANG 

机构地区：[1]School of Computer Science and Technology,Shaanxi Province Key Laboratory of Computer Network,Xi’an Jiaotong University,Xi’an 710049,China [2]School of Software,Henan University,Kaifeng 475001,China [3]School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China

出　　处：《Frontiers of Computer Science》2023年第4期127-139,共13页中国计算机科学前沿（英文版）

基　　金：supported by the Key Science and Technology Project of Henan Province(201300210400);National Key Research and Development Project(2018YFB1800304);National Natural Science Foundation of China(61762058),Fundamental Research Funds for the Central Universities(xzy012020112);Natural Science Foundation of Gansu Province(21JR7RA282).

摘　　要：With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

关 键 词：crowdsensing edge computing devices blockchain quality assessment reinforcement learning 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]