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

doi:10.1007/s11704-022-2083-8

Front. Comput. Sci.

2023, Vol. 17

Issue (4) : 174503 https://doi.org/10.1007/s11704-022-2083-8

RESEARCH ARTICLE

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

Jian AN¹, Siyuan WU¹, Xiaolin GUI¹, Xin HE²(

), Xuejun ZHANG³

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

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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.

Keywords crowdsensing edge computing devices blockchain quality assessment reinforcement learning

Corresponding Author(s): Xin HE

Just Accepted Date: 15 September 2022 Issue Date: 25 December 2022

Cite this article:

Jian AN,Siyuan WU,Xiaolin GUI, et al. A blockchain-based framework for data quality in edge-computing-enabled crowdsensing[J]. Front. Comput. Sci., 2023, 17(4): 174503.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-2083-8
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I4/174503

Fig.1 Blockchain-based framework of BFEC

Notation	Description
$N$	The number of involved ECDs
$d i f f i, D i f f$	The difficulty diff_i of ED_i, the difficulty set $D i f f$
$p i, P$	The number of participants $p i$ of ED_i, the number of participants set $P$
$q i, Q$	The number of data attributes $q i$ of ED_i, the number of data attributes $Q$
h, H	Task number $h$ , the current task number $H$
$x i, D$	The sensing data $x i$ of D, the sensing data set $D$
$y i, Y$	The label $y i$ of sensing data $x i$ , the label data set $Y$
$a t t r i$ , $A t t r$	Data attribute attr_i of Attr, the data attribute set Attr
$n i m p i, N i m p$	The importance nimp_i of attr_i,,the importance set Nimp
$c o n t i, C o n t$	The contribution cont_i of attr_i, the contribution set Cont

Tab.1 Frequently used notations

Fig.2 The training process of DVRL

Fig.3 The process of IPC

Tab.2 Sources of data sets

Fig.4 Average runtime comparison

Fig.5 Accuracy change trend on Blog data set

Fig.6 Accuracy change trend on Wine Quality data set

Fig.7 Corrupted sample discovery on three datasets

Fig.8 Weighted learning performance

Tab.3 Learning performance of data sets

Fig.9 Performance trend on adult data set

Fig.10 Performance trend on Blog data set

Tab.4 Accuracy comparision on two data sets

Tab.5 RMSPE of baseline and DVRL

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