期刊
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
卷 29, 期 1, 页码 198-211出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2017.2712630
关键词
Information integration; distributed sensing system; participatory sensing; crowd sensing; social sensing; quality
资金
- US National Science Foundation [CNS-1566374, CNS-1652503, IIS-1319973, IIS-1553411]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1566374] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1618481] Funding Source: National Science Foundation
In this paper, we present GDA, a generalized decision aggregation framework that integrates information from distributed sensor nodes for decision making in a resource efficient manner. Different from traditional approaches, our proposed GDA framework is able to not only estimate the reliability of each sensor, but also take advantage of its confidence information, and thus achieves higher decision accuracy. Targeting generalized problem domains, our framework can naturally handle the scenarios where different sensor nodes observe different sets of events whose numbers of possible classes may also be different. GDA also makes no assumption about the availability level of ground truth label information, while being able to take advantage of any if present. For these reasons, our approach can be applied to a much broader spectrum of sensing scenarios. In this paper, we also propose two extensions of the GDA framework, i.e., incremental GDA (I-GDA) and parallel GDA (P-GDA) to deal with streaming and large-scale data. The advantages of our proposed methods are demonstrated through both theoretic analysis and extensive experiments.
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