基于多目标效用优化的分布式数据交易算法

doi:10.11959/j.issn.1000-436x.2021034

Abstract

Abstract:

The traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essential to design an effective data trading framework.Therefore, a distributed data trading framework based on consortium blockchain was proposed, which realized P2P data trading without relying on a third party.Aiming at the problem that existing data trading models only consider the factors of the data itself and ignore the factors related to user tasks, a bi-level multi-objective optimization model was constructed based on multi-dimensional factors, such as data quality, data attributes, attribute relevance and consumer competition, to optimize the utilities of data provider (DP) and data consumer (DC).To solve the above model, an improved multi-objective genetic algorithm-collaborative NSGAII was proposed, calculated by the cooperation of DP, DC and data aggregator (AG).The simulation results show that the collaborative NSGAII achieves better performance in terms of the utilities of DP and DC, thus realizing more effective data trading.

Key words: consortium blockchain, distributed data trading, optimization matching model, multi-objective genetic algo-rithm

CLC Number:

TP399

Xiaohong HUANG, Yong ZHANG, Desheng SHAN, Yekui QIAN, Lu HAN, Dandan LI, Qun CONG. Distributed data trading algorithm based on multi-objective utility optimization[J]. Journal on Communications, 2021, 42(2): 52-63.

Figures/Tables 19

参数	含义
A	AG的集合
P	a_i中DP的集合
C	a_i中DC的集合
$p_{j}$	P中的第 j位DP
c_k	C 中的第k位DC
$M_{j}^{p}$	p _j数据的属性集合
$M_{k}^{c}$	c _k 的数据属性需求集合
${PC}_{j, k}$	p _j为c_k 提供的数据产品
$M_{j, k}$	PC _j,k的属性集合
$N_{j}$	p _j的数据量
$n_{k}^{\min}$	c _k 想要购买完全满足其属性需求的最小数据量
$n_{k}^{\max}$	c _k 想要购买完全满足其属性需求的最大数据量
β	数据质量评估的抽样比例
r_j	由a_i给出的 p_j数据的质量评估
$q_{j, k}$	PC _j,k对c_k 的可用性
$x_{j, k}$	c _k 从 p_j处购买数据的数量
$w_{j, k}$	c _k 从 p_j处购买数据的单价

DP	$\| M_{j}^{p} \|$	N _j /单元	数据噪声等级
DP₁	10	17	Ⅳ
DP₂	17	35	Ⅰ
DP₃	15	30	Ⅱ
DP₄	12	30	Ⅲ
DP₅	14	30	Ⅱ
DP₆	10	20	Ⅳ

DC	$\| M_{k}^{c} \|$	$n_{k}^{\min}$ /单元	λ_k	q_{j ,k}是否熟悉
DC₁	13	27	0.85	是
DC₂	10	25	0.90	是
DC₃	15	30	0.55	否
DC₄	12	27	0.82	是
DC₅	15	29	0.62	否
DC₆	16	26	0.73	否

References 28

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