Selective Sampling for Sensor Type Classification in Buildings

Ma, Jing; Hong, Dezhi; Wang, Hongning

doi:10.1109/ipsn48710.2020.00028

Title: Selective Sampling for Sensor Type Classification in Buildings

Conference · Tue Jun 09 00:00:00 EDT 2020 · 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

DOI:https://doi.org/10.1109/ipsn48710.2020.00028· OSTI ID:1822657

Ma, Jing; Hong, Dezhi; Wang, Hongning

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

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Research Organization:: University of Virginia

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

DOE Contract Number:: EE0008227

OSTI ID:: 1822657

Report Number(s):: DOE-UVA-0008227-7

Journal Information:: 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Conference: The 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Sydney, NSW, Australia, 21-24 April 2020

Country of Publication:: United States

Language:: English

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