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

Abstract

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 tamper-resistance. 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.

Graphical abstract

Keywords

crowdsensing / edge computing devices / blockchain / quality assessment / reinforcement learning

Jian An received his BS and MS degrees in Computer Science from Xinjiang University, China and PhD degrees in Computer Science from Xi’an Jiaotong University, China in 2005, 2008, and 2013, respectively. He is a lecture in the Department of Computer Science and Technology at Xi’an Jiaotong University, China. He is a researcher at the Key Laboratory of Computer Network in Xi’an Jiaotong University, China. He has been researching on the social network, service computing and Internet of Things. His team has won the Science and Technology Progress Award of Shaanxi, and a number of National Natural Science Foundation Awards. He has authored or co-authored more than 20 articles for scientific books, journals, and conferences

Siyuan Wu received her BS degree in Software Engineering from Xidian University, China in 2020. She is currently working towards the master’s degree at Xi’an Jiaotong University, China. Her research interests include quality assessment and mobile crowdsensing

Xiaolin Gui is a professor in the Department of Computer Science and Technology at Xi’an Jiaotong University, China. He services as a Deputy Dean of School of Electronic and Information from 2013. He is also Director of the Key Lab of Computer Network of Shaanxi Provice, China from 2008. He leads the Center for Grid and Trusted Computing (CGTC). Professor Gui graduated with a BSc degree in Computer from Xi’an Jiaotong University, China and received MSc and PhD degrees in Computer Science from Xi’an Jiaotong University, China in 1993 and 2001. Since joining Xi’an Jiaotong University in 1988, he has been an active researcher in network computing, network security, and wireless networks

Xin He is a professor in the Department of Software at Henan University, China. He services as a dean of Department of Network Engineering from 2012. He graduated with a BSc and MSc degrees in Computer from Henan University, China and received PhD degree in Computer Science from Xi’an Jiaotong University, China. Since 2002, he has been an active researcher in network computing, and wireless networks. He has authored or co-authored more than 30 articles for scientific books, journals, and conferences

Xuejun Zhang is currently a professor in the Department of Electronic and Information Engineering at Lanzhou Jiaotong University, China. He is also a senior member of CCF and ACM. His main research interests include user privacy measurement and protection in location services, network security, data privacy, and machine learning

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Acknowledgements

This work was 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), and Natural Science Foundation of Gansu Province (21JR7RA282).

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