Online Spatial Crowdsensing With Expertise-Aware Truth Inference and Task Allocation
Online Spatial Crowdsensing With Expertise-Aware Truth Inference and Task Allocation
复制标题
具有专业知识意识的真理推理和任务分配的在线空间群智感知
DOI：
10.1109/jsac.2021.3126045
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发表时间：
2022
影响因子：
16.4
通讯作者：
Xinbing Wang
中科院分区：
文献类型：
--
作者：
Xiong Wang;Riheng Jia;Luoyi Fu;Haiming Jin;Xiaohua Tian;Xiaoying Gan;Xinbing Wang
新兴的人群传感范式支持大量传感应用，其中数据收集和真相推理的基本问题受到广泛关注。现有的工作已经设计了多种技术来发现真理从收集的噪声数据，但他们经常忽略工人的各种专业知识和crowdsensing系统的动态信息，从而导致错误的估计真理和不合格的传感数据。在本文中，我们设计了一个在线的位置感知crowdsensing系统，以准确地估计真相和有效地分配任务。具体来说，我们统一不同类型的数值和分类任务的基础上概率图模型，然后提出无监督学习方法，可以动态地推断地面真理和各种工人的专业知识在同一时间。此外，我们开发在线任务分配方案，逐步收集高质量的数据，考虑位置意识和推断工人的专业知识。特别地，将复杂的任务分配问题转化为概率提高和熵降低的加法形式，从而通过线性选择计算复杂度较低的工作者-任务对来解决分配问题。最后，我们使用智能手机收集的两个数据集进行了广泛的评估，结果证明了我们的算法优于最先进的方法。
Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems of data collection and truth inference. Existing works have devised manifold techniques to discover truth from collected noisy data, but they frequently ignore various expertise of workers and dynamic information of crowdsensing system, thus leading to error-prone estimated truth and unqualified sensing data. In this paper, we design an online location-aware crowdsensing system to accurately estimate truth and efficiently assign tasks. Specifically, we unify diverse types of numerical and categorical tasks based on probabilistic graphical model, and then propose unsupervised learning methods which can dynamically infer ground truth and various worker expertise at the same time. Furthermore, we develop online task allocation schemes to gradually gather high quality data considering location awareness and inferred worker expertise. In particular, we convert the complicated task allocation into the additive form of probability improvement and entropy reduction, thereby solving the allocation problem via linearly selecting worker-task pair with low computation complexity. We finally carry out extensive evaluations using two datasets collected by our smartphones, where results demonstrate the superiority of our algorithms over the state-of-the-art approaches.