基于知识图谱的移动众测时空任务分配模型

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Bingxu Zhao, Hongbin Dong, Dongmei Yang
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引用次数: 0

摘要

随着无线网络的日益普及和智能城市的发展,移动众包系统(MCS)已经成为一种自动为工人分配时空任务的框架。移动众包的研究为社区服务和城市路线规划做出了宝贵的研究贡献。然而,以前的算法在有效解决具有大量空间数据的任务分配问题方面面临挑战。在本文中,我们提出了一种使用知识图的时空任务分配问题的新解决方案。首先,我们构造了一个鲁棒的时空知识图(STKG),并使用知识图嵌入算法来学习节点和边的表示。接下来,我们利用这些表示来构建任务转换图,这是一个加权的、基于学习的图,它突出了每个任务的重要邻居。然后,我们应用简化的图卷积网络(GCN)和基于RNN的模型来增强任务表示,并在任务转换图上捕获顺序转换模式。此外,我们设计了一个相似函数来促进个性化任务分配。通过实验结果,我们证明,当在三个真实数据集上测试时,与现有方法相比,我们的解决方案实现了更高的精度。这些研究结果具有重要意义,因为它们有助于时空任务分配准确性提高18.01%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Spatio-Temporal Task Allocation Model in Mobile Crowdsensing Based on Knowledge Graph
With the increasing popularity of wireless networks and the development of smart cities, the Mobile Crowdsourcing System (MCS) has emerged as a framework for automatically assigning spatiotemporal tasks to workers. The study of mobile crowdsourcing makes a valuable research contribution to community service and urban route planning. However, previous algorithms have faced challenges in effectively addressing task allocation issues with massive spatial data. In this paper, we propose a novel solution to the spatiotemporal task allocation problem using a knowledge graph. Firstly, we construct a robust spatiotemporal knowledge graph (STKG) and employ a knowledge graph embedding algorithm to learn the representations of nodes and edges. Next, we utilize these representations to build a task transition graph, which is a weighted and learning-based graph that highlights important neighbors for each task. We then apply a simplified Graph Convolutional Network (GCN) and an RNN-based model to enhance task representations and capture sequential transition patterns on the task transition graph. Furthermore, we design a similarity function to facilitate personalized task allocation. Through experimental results, we demonstrate that our solution achieves higher accuracy compared to existing approaches when tested on three real datasets. These research findings are significant as they contribute to an 18.01% improvement in spatiotemporal task allocation accuracy.
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来源期刊
Smart Cities
Smart Cities Multiple-
CiteScore
11.20
自引率
6.20%
发文量
0
审稿时长
11 weeks
期刊介绍: Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.
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