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

doi:10.1007/s11704-022-2083-8

Frontiers of Computer Science

2023, Vol. 17

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

本期目录

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.

Key words： crowdsensing edge computing devices blockchain quality assessment reinforcement learning

收稿日期: 2022-02-09 出版日期: 2022-12-25

Corresponding Author(s): Xin HE

引用本文:

. [J]. Frontiers of Computer Science, 2023, 17(4): 174503.
Jian AN, Siyuan WU, Xiaolin GUI, Xin HE, Xuejun ZHANG. A blockchain-based framework for data quality in edge-computing-enabled crowdsensing. Front. Comput. Sci., 2023, 17(4): 174503.

链接本文:

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

Fig.1

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

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